Conventional and antibody-enhanced DENV infection of human macrophages induces differential immunotranscriptomic profiles
Dengue virus (DENV) is a mosquito-borne flavivirus and globally relevant human pathogen accounting for an estimated 390 million infections yearly ( , ). DENV exists as four genetically and immunologically distinct serotypes (DENV-1 to -4) which co-circulate in endemic areas, primarily in tropical and subtropical regions. Clinical presentations of dengue range from an acute febrile illness to life-threatening hemorrhagic fever characterized by plasma leakage, hemorrhage, and severe shock ( , , ). Correlates of risk for the development of severe dengue are poorly defined, although the best described risk factor for severe disease is a secondary DENV infection with a heterologous serotype ( , ). One of the mechanisms believed to contribute to the increased risk of severe disease in secondary heterologous infection is antibody-dependent enhancement (ADE), a process by which pre-existing antibodies facilitate viral entry into phagocytic leukocytes by Fc gamma receptor (FcγR)-mediated endocytosis ( ). The presence of discrete pathogenic mechanisms specific to ADE compared to conventional infection suggests the opportunity for novel therapeutics leveraging strategies that manipulate host factors critical to infection by ADE. Thus, improving our understanding of mechanisms of ADE-mediated pathogenesis may provide insight for treatment approaches, vaccine development, and severe disease correlates for prognostication. ADE has been demonstrated to result in elevated inflammatory pathology and increased virion production on a per-cell basis using a variety of in vitro cell culture systems ( ). The elevated pathology observed in these models is thought to be in part mediated by increased efficiency of the FcγR interaction with DENV/IgG immune complexes relative to the interaction of DENV alone with canonical entry receptors such as DC-SIGN and the mannose receptor ( ). This high-affinity receptor interaction is thought to result in increased burden of infection due to increased virion entry to susceptible cells and has been recognized as a hallmark “cell-extrinsic” feature of ADE. The cell-extrinsic effects of ADE are most often associated with in vivo DENV infections, where more and different cells are thought to be infected in the presence of enhancing antibody. In contrast, “cell intrinsic” response to infection by ADE has historically referred to altered signaling and cellular responses to infection which facilitate viral replication in ADE-infected cells ( ). Prior work has identified discrete transcriptomic and inflammatory signatures of intrinsic ADE, including the absence of an early interferon response and altered inflammatory signaling in cells infected by ADE ( , , ). While the precise mechanisms underpinning these altered responses to infection remain incompletely understood, prior work points to preferential expression of anti-inflammatory cytokines (such as IL-10) and evasion of innate immune sensing (RIG-I, TLR) ( , ). However, the existence of a cell-intrinsic ADE response—and our understanding of the transcriptomic mechanisms governing this response—has been a point of contention in the literature. The use of many different cell models and a variety of techniques for characterizing heterogeneous cultures has led to discordance in the proposed intrinsic mechanisms of ADE. It remains unclear whether the effects of enhanced infection occur primarily due to increased viral entry and exit, and how infection-elicited transcriptional profiles differ by route of viral entry. Studies seeking to differentiate the cell-extrinsic from cell-intrinsic response to ADE during DENV infection have largely been impacted by technical limitations. However, recent advances in single-cell RNA sequencing (scRNAseq) technology, which allow for the capture of viral RNA in addition to host-derived transcripts, have afforded the opportunity to identify and characterize features of infected cells in heterogeneous samples ( ). In this work, we applied an scRNAseq approach to specifically characterize the spectrum of cell-intrinsic responses to DENV-2 infection in human monocyte-derived macrophages cultured under enhancing (ADE) or non-enhancing (conventional infection) conditions. This work identifies discrete transcriptional features of cells infected under different conditions of viral entry, where infected cells in ADE conditions demonstrated downregulated cytokine and interferon responses compared to those infected by conventional receptor-mediated entry. A distinct second population of presumed uninfected/bystander cells in ADE cultures contrastingly displayed enrichment of immune signaling and responses, including increased cytokine production and antigen presentation. Together, these data provide insight into unique cellular responses under enhancing conditions compared to conventional receptor-mediated entry and their role in DENV pathogenesis. 

scRNAseq identifies distinct cell populations according to condition and inferred infection status To characterize the transcriptional profiles of DENV-2-infected and bystander cells in various infection conditions, macrophages were differentiated from primary human peripheral blood mononuclear cell (PBMC)-derived monocytes and infected with DENV-2 (strain NGC) in enhancing (ADE) or non-enhancing conditions, along with an uninfected control sample (Materials and Methods , ). The 10x Genomics 5´ scRNAseq assay was used for single-cell partitioning and library generation, and a concatenated DENV-2/human reference genome was used to quantify both host cell and viral RNA transcripts. Base clustering of transcriptomic data identified three distinct clusters across cells from all three experimental conditions ( ). These clusters corresponded broadly to cells from the control condition (cluster 1) and two clusters (clusters 0 and 2) with cells from both the conventional infection (DENV-2) and ADE (DENV-2 + IgG) conditions ( ; ). We observed the presence of elevated DENV-2 RNA in cluster 2, and a low-level DENV-2 RNA signal in all cells in infected cultures ( ; ). This is consistent with previous reports indicating the presence of ambient RNA in bystander cells within infected cultures and is attributable to cell lysis in infection conditions and subsequent carryover into the droplet-generation step of scRNAseq library preparation ( , ). Accordingly, cell infection state was assigned in this analysis based on unbiased cell clustering in conjugation with DENV-2 RNA content rather than the presence or absence of DENV-2 RNA as a single transcript. Based on cell clustering and patterns of DENV-2 RNA expression, cells within cluster 2 were designated as “infected” and cells in cluster 0 as “bystanders” ( ). These infection state designations aligned with a bimodal distribution of DENV-2 RNA expression ( ; ), with the “infected” cell exhibiting a uniformly higher DENV-2 RNA content than their bystander counterparts. Using these infection state designations, 12.8% of cells in the DENV-2 condition fell within the infected cell cluster, compared to 49.6% infection in DENV-2 + IgG (ADE) conditions ( ). Consistent with prior reports, we observed an approximate 8:1 ratio of positive- to negative-sense DENV-2 RNA within DENV-infected cells, the abundance of which were both positively correlated across all infected cells ( r = 0.96) ( ; ). DENV-2-infected cells in ADE conditions had a modest but statistically significantly higher DENV-2 RNA content on a per-cell basis compared to cells infected under non-enhancing conditions (logFC = 0.480, adjusted P = 6.08 e − 7) ( ). These findings support that we were successful in the capture of DENV-2 and host cell transcripts under both conventional and IgG-enhanced infection conditions, enabling the differentiation of cell-intrinsic from cell-extrinsic ADE effects. Infected and bystander cells demonstrate distinct gene expression profiles To initially assess the differences in the transcriptomic profiles of DENV-2-infected and -uninfected/bystander cells within heterogeneous infection cultures, differential gene expression analysis was performed between DENV-2-infected or -uninfected/bystander cells and control cells ( ). This analysis identified a total of 906 upregulated and 908 downregulated differentially expressed genes (DEGs) in infected cells compared to control ( and ). In bystander cells compared to control, 1,270 genes were upregulated and 1,479 were downregulated ( and ). A core set of 639 upregulated and 819 downregulated genes were observed to be differentially expressed by cells in DENV-2-inoculated cultures relative to the control culture, irrespective of infection designation ( ; ). However, a number of DEGs were selectively differentially expressed within either the infected cells (352 genes) or uninfected/bystander cells (1,291 genes) relative to control ( ; and ). Many of these infection-associated DEGs—either within the DENV-2-infected cells or the uninfected bystander cells within the same culture—correspond to canonical inflammatory/anti-viral gene products, including cytokines, chemokines, immune signaling receptors, and interferon-stimulated genes (ISGs) ( ). DENV-2-infected cells within the heterogeneous culture demonstrated selective upregulated expression of cytokines and chemokines such as IL1A, IL1B, IFNB1, TNF, and IL6, whereas bystander cells demonstrated upregulation of ISGs such as MX1, MX2, IFIT1, and OAS 1 ( ). Overall, these transcriptomic changes are consistent with expected signaling relationships, indicating activated pro-inflammatory signaling and interferon expression in infected cells, and response to these inflammatory cues by bystander-uninfected cells in culture. In order to determine associations of these gene expression changes as they relate to specific biological processes within DENV-infected and bystander macrophages in DENV-inoculated cultures, we performed a pathway enrichment analysis using Ingenuity Pathway Analysis (IPA). Upregulated pathways in infected cells included elevated interleukin-10 signaling, pattern recognition receptor responses, growth factor signaling, and signaling associated with pathogenic processes (pulmonary fibrosis, hepatic fibrosis, osteoarthritis, cancer) ( ; ). IPA graphical summary analysis summarized the overall phenotype of infected cells as promoting proliferation and differentiation of lymphocytes and antigen presenting cells, immune cell recruitment, and differentiation of phagocytes ( ). The graphical summary of infected pathways interestingly emphasized the predicted role of TLR signaling and activation of IL-1β in driving these responses. Together, this analysis suggests an activated transcriptional profile specific to infected cells activated by innate immune receptors which promotes cytokine secretion, type I interferon induction, and proliferation, recruitment, and growth of leukocytes. In uninfected/bystander cells compared to control, IPA predicted many differentially regulated pathways, where similar to in DEG analysis, both the number of pathways and magnitude of these responses indicate a more prominent response in bystander than infected cells ( ; ). Of these pathways, top upregulated processes included activation of neutrophil degranulation, interferon signaling, and multiple pathways indicating activation of inflammation and anti-viral responses ( ). This was emphasized in graphical summary, where type I IFN (IFNA1, IFNA2, IFNA13) activation of IFNAR1/2 was predicted to induce anti-viral and anti-microbial responses ( ). These data also identified a role for IRF3- and IRF7-mediated activation of IFN signaling in bystander cells. Altogether, these findings support that the identified infected and uninfected/bystander cell populations represent transcriptionally distinct groups with expected immunological features according to infection state. Inflammatory signaling is decreased in macrophages infected in ADE conditions Having defined the transcriptional profiles associated with DENV-2 infection irrespective of route of infection, we next wanted to determine if there was any difference in DENV infection-elicited transcriptional profile between cells infected via ADE or conventional entry routes. Accordingly, we performed differential gene expression analysis specifically on cells within the DENV-2-infected cluster, binning by infection condition (DENV-2 + IgG vs DENV-2 only) ( ). This analysis identified a total of 162 differentially expressed genes within the infected cell cluster between the two infection conditions, of which 45 genes were significantly upregulated, and 117 genes were significantly downregulated in cells cultured in ADE compared to conventional infection conditions ( and ). Top downregulated DEGs included many chemokine ligands such as CCL8 , CCL2 , CXCL10 , CCL7 , and CXCL11 , and immune signaling associated genes such as P2RX7, STAT1 , and JAK2 ( ; ). Although there appeared to be overall immunosuppressive changes to gene expression in cells infected in ADE compared to conventional infection conditions, select immune signaling molecules were enriched, including top upregulated DEGs CCR7 , ISG15 , IL23A , and TRAF ( ). Pathway analysis was performed using an input of DEGs between infected cells in ADE compared to conventional DENV-2 infection conditions. In infected cells, IPA predicted a predominantly suppressed phenotype in ADE compared to conventionally infected cells ( ; ). Pathway analysis notably indicated downregulation of the inflammatory response in ADE-infected cells, where downregulated pathways included: “neutrophil degranulation,” “pathogen induced cytokine storm signaling pathway,” “interferon gamma signaling,” and “macrophage classical activation signalling pathway” ( ). Pathways involved in leukocyte recruitment, activation, and proliferation were downregulated compared to cells infected in conventional infection conditions. There also appeared to be an enriched transcriptional signature of mitochondrial dysfunction in cells infected in ADE conditions, and downregulated pathways including “electron transport, ATP synthesis and heat production by uncoupling proteins,” and “oxidative phosphorylation” ( ). Of the few upregulated pathways in cells infected in ADE conditions, “IL-12 Signaling and production in macrophages” interestingly emerged as a top pathway. In graphical summary analysis, these processes were summarized as being driven by decreases in cytokine production such as IL-1α, IL-1β, and TNF, and decreased interferon response signaling by STAT1 and JAK2 ( ). These reflect both a decrease in innate anti-viral activation of cytokine responses as well as secondary immune response to IFN production. Together, these processes were highlighted as contributing to downregulated recruitment, activation, and cytotoxicity of leukocytes, IFN-γ, and cytokine storm signaling, as determined by bioinformatic imputation from IPA graphical summary analysis ( ). The observed decrease in transcriptional pro-inflammatory processes in macrophages infected in ADE conditions might indicate mechanisms of attenuated immune response in cells infected by this route of entry which facilitate viral replication and contribute to enhanced viral burden. To confirm that these observations were not driven by misclassification of infected and bystander cells, we also compared the expression profiles of all cells in ADE conditions to all those in DENV-alone conditions. This pseudo-bulk analysis allowed for a more direct comparison of these data to the literature where bulk analysis was performed ( ). Key infection status-specific findings such as decreased chemokine signaling and immune cell recruitment were also identified in bulk analysis ( ). Additionally, this analysis recapitulated published findings which were observed at the bulk level with respect to interferon signaling ( , , ). Pathways specifically identified as downregulated in cells from ADE compared to conventional infection conditions included “antiviral response,” “interferon alpha beta signaling,” and “interferon signaling” ( ). Altogether, bulk analysis of these data reinforces central proposed mechanisms of intrinsic ADE. Beyond this, the discrete differences observed in bulk, compared to cell population-specific analysis, indicate the relevance of additional granularity according to infection status for defining this response. ADE-associated gene expression changes are independent of DENV RNA content Given that infected cells in ADE conditions demonstrated increased mean viral RNA on a per-cell basis, we next sought to determine whether the transcriptomic changes observed in DENV-2-infected cells under ADE conditions were simply due to increased viral burden within cells. To this end, correlation analysis was performed between DENV-2 RNA content and expression of all genes in the data set within the infected cells. While we cannot dismiss the possibility that differing protein content in these cells may correlate with the abundance of host factors, in this analysis, we use the abundance of positive-sense genomic RNA as a proxy for infection burden. For this analysis, Pearson correlation analysis was performed on a per-cell basis to individually compare the expression of each gene identified in the data set to the DENV-2 RNA content in the same cell. Corresponding r 2 values for each of these comparisons were used to determine the correlation strength. Across all host genes captured in this analysis, no significant correlation (all r 2 values <0.6) between DENV-2 (+) RNA expression and the expression of cellular transcripts was observed on a per-cell basis ( ). This observation was also true for the expression of DENV-2 (−) RNA and cellular transcripts. Specific visualization of select differentially expressed genes between infected cells in ADE compared to conventional infection conditions further emphasized the lack of cell-intrinsic correlation between DENV-2 RNA content and differential gene expression. ( ). These findings indicate that transcriptomic differences are likely not driven by differences in DENV-2 RNA content and support that these changes are instead reflective of intrinsic transcriptional differences in ADE and conventional infection. Bystander cells in ADE conditions have heightened anti-viral responses Although most of the work defining intrinsic ADE has focused on characterizing either bulk cellular responses or those of infected cells, the potentially unique features of bystander cells in ADE conditions have yet to be explored. We identified discrete differences in the responses of infected and bystander cells across conditions, and therefore postulated that bystander cells in different infection conditions also possessed unique transcriptomic features. We next looked specifically at bystander cells as designated by initial clustering and imputed cell labeling to evaluate gene expression differences between cells in ADE (DENV-2 + IgG) and conventional infection (DENV-2) conditions ( ). In this comparison, DEG analysis identified 467 differentially expressed genes, of which 238 were upregulated and 229 were downregulated ( and ). Notably, these changes along with those identified in pathway analysis indicated that bystander cells harbored more distinct changes between infection conditions than infected cells. Among top upregulated DEGs were TYROBP, ISG15, IFITM3, RPL28, CD74, and CCR7 ( ). Bystander cells in ADE compared to conventional infection conditions uniquely demonstrated significant upregulation of MHC expression. Top downregulated DEGs in bystander cells indicated a decreased enrichment of certain pro-inflammatory cytokines and signaling molecules in ADE compared to conventional infection conditions, including CXCL11, CXCL10, CCL2, DOCK4, STAT1 , and JAK2 ( ). Notably, while many gene expression changes were unique to infected or bystander cells, a number of upregulated or downregulated genes were common between infected and bystander cells in ADE compared to conventional infection conditions. For example, TYROBP, CCR7, CXCL10, and CXCL11 emerged as top DEGs in both infected and bystander cells in ADE compared to conventional infection. In bystander cells, IPA identified enriched pathways including “neutrophil degranulation” and “pathogen induced cytokine storm signaling,” in addition to a number of pathways relating to translation initiation and elongation ( ). Pathway analysis also highlighted a role for antigen presentation pathway upregulation, consistent with the MHC gene expression changes described ( ). Downregulated IL-10 signaling emerged as a top downregulated pathway in bystander cells in ADE conditions, and similar to infected cells, a prominent role for downregulation of metabolic processes involving electron transport and oxidative phosphorylation ( ). Integration of pathway enrichment data in graphical summary demonstrated increased cell viability of phagocytes and APCs, differentiation of lymphocytes, and EIF2 signaling in bystander cells in ADE conditions. These changes were driven by increases in transcript production of various signaling molecules such as HIF1A, EGFR, IL22, IFNE, IFNG, TLR4, IL6, and IL1B ( ). Compared to bystander cells in conventional infection conditions, those in ADE conditions demonstrated an overall activating response by these processes. These findings indicate that in ADE conditions, alternative signaling by infected cells or increased infection burden contributes to distinct transcriptomic differences in bystander cell responses which may provide insight to our understanding of cellular responses to DENV-2 infection. 

To characterize the transcriptional profiles of DENV-2-infected and bystander cells in various infection conditions, macrophages were differentiated from primary human peripheral blood mononuclear cell (PBMC)-derived monocytes and infected with DENV-2 (strain NGC) in enhancing (ADE) or non-enhancing conditions, along with an uninfected control sample (Materials and Methods , ). The 10x Genomics 5´ scRNAseq assay was used for single-cell partitioning and library generation, and a concatenated DENV-2/human reference genome was used to quantify both host cell and viral RNA transcripts. Base clustering of transcriptomic data identified three distinct clusters across cells from all three experimental conditions ( ). These clusters corresponded broadly to cells from the control condition (cluster 1) and two clusters (clusters 0 and 2) with cells from both the conventional infection (DENV-2) and ADE (DENV-2 + IgG) conditions ( ; ). We observed the presence of elevated DENV-2 RNA in cluster 2, and a low-level DENV-2 RNA signal in all cells in infected cultures ( ; ). This is consistent with previous reports indicating the presence of ambient RNA in bystander cells within infected cultures and is attributable to cell lysis in infection conditions and subsequent carryover into the droplet-generation step of scRNAseq library preparation ( , ). Accordingly, cell infection state was assigned in this analysis based on unbiased cell clustering in conjugation with DENV-2 RNA content rather than the presence or absence of DENV-2 RNA as a single transcript. Based on cell clustering and patterns of DENV-2 RNA expression, cells within cluster 2 were designated as “infected” and cells in cluster 0 as “bystanders” ( ). These infection state designations aligned with a bimodal distribution of DENV-2 RNA expression ( ; ), with the “infected” cell exhibiting a uniformly higher DENV-2 RNA content than their bystander counterparts. Using these infection state designations, 12.8% of cells in the DENV-2 condition fell within the infected cell cluster, compared to 49.6% infection in DENV-2 + IgG (ADE) conditions ( ). Consistent with prior reports, we observed an approximate 8:1 ratio of positive- to negative-sense DENV-2 RNA within DENV-infected cells, the abundance of which were both positively correlated across all infected cells ( r = 0.96) ( ; ). DENV-2-infected cells in ADE conditions had a modest but statistically significantly higher DENV-2 RNA content on a per-cell basis compared to cells infected under non-enhancing conditions (logFC = 0.480, adjusted P = 6.08 e − 7) ( ). These findings support that we were successful in the capture of DENV-2 and host cell transcripts under both conventional and IgG-enhanced infection conditions, enabling the differentiation of cell-intrinsic from cell-extrinsic ADE effects. 

To initially assess the differences in the transcriptomic profiles of DENV-2-infected and -uninfected/bystander cells within heterogeneous infection cultures, differential gene expression analysis was performed between DENV-2-infected or -uninfected/bystander cells and control cells ( ). This analysis identified a total of 906 upregulated and 908 downregulated differentially expressed genes (DEGs) in infected cells compared to control ( and ). In bystander cells compared to control, 1,270 genes were upregulated and 1,479 were downregulated ( and ). A core set of 639 upregulated and 819 downregulated genes were observed to be differentially expressed by cells in DENV-2-inoculated cultures relative to the control culture, irrespective of infection designation ( ; ). However, a number of DEGs were selectively differentially expressed within either the infected cells (352 genes) or uninfected/bystander cells (1,291 genes) relative to control ( ; and ). Many of these infection-associated DEGs—either within the DENV-2-infected cells or the uninfected bystander cells within the same culture—correspond to canonical inflammatory/anti-viral gene products, including cytokines, chemokines, immune signaling receptors, and interferon-stimulated genes (ISGs) ( ). DENV-2-infected cells within the heterogeneous culture demonstrated selective upregulated expression of cytokines and chemokines such as IL1A, IL1B, IFNB1, TNF, and IL6, whereas bystander cells demonstrated upregulation of ISGs such as MX1, MX2, IFIT1, and OAS 1 ( ). Overall, these transcriptomic changes are consistent with expected signaling relationships, indicating activated pro-inflammatory signaling and interferon expression in infected cells, and response to these inflammatory cues by bystander-uninfected cells in culture. In order to determine associations of these gene expression changes as they relate to specific biological processes within DENV-infected and bystander macrophages in DENV-inoculated cultures, we performed a pathway enrichment analysis using Ingenuity Pathway Analysis (IPA). Upregulated pathways in infected cells included elevated interleukin-10 signaling, pattern recognition receptor responses, growth factor signaling, and signaling associated with pathogenic processes (pulmonary fibrosis, hepatic fibrosis, osteoarthritis, cancer) ( ; ). IPA graphical summary analysis summarized the overall phenotype of infected cells as promoting proliferation and differentiation of lymphocytes and antigen presenting cells, immune cell recruitment, and differentiation of phagocytes ( ). The graphical summary of infected pathways interestingly emphasized the predicted role of TLR signaling and activation of IL-1β in driving these responses. Together, this analysis suggests an activated transcriptional profile specific to infected cells activated by innate immune receptors which promotes cytokine secretion, type I interferon induction, and proliferation, recruitment, and growth of leukocytes. In uninfected/bystander cells compared to control, IPA predicted many differentially regulated pathways, where similar to in DEG analysis, both the number of pathways and magnitude of these responses indicate a more prominent response in bystander than infected cells ( ; ). Of these pathways, top upregulated processes included activation of neutrophil degranulation, interferon signaling, and multiple pathways indicating activation of inflammation and anti-viral responses ( ). This was emphasized in graphical summary, where type I IFN (IFNA1, IFNA2, IFNA13) activation of IFNAR1/2 was predicted to induce anti-viral and anti-microbial responses ( ). These data also identified a role for IRF3- and IRF7-mediated activation of IFN signaling in bystander cells. Altogether, these findings support that the identified infected and uninfected/bystander cell populations represent transcriptionally distinct groups with expected immunological features according to infection state. 

Having defined the transcriptional profiles associated with DENV-2 infection irrespective of route of infection, we next wanted to determine if there was any difference in DENV infection-elicited transcriptional profile between cells infected via ADE or conventional entry routes. Accordingly, we performed differential gene expression analysis specifically on cells within the DENV-2-infected cluster, binning by infection condition (DENV-2 + IgG vs DENV-2 only) ( ). This analysis identified a total of 162 differentially expressed genes within the infected cell cluster between the two infection conditions, of which 45 genes were significantly upregulated, and 117 genes were significantly downregulated in cells cultured in ADE compared to conventional infection conditions ( and ). Top downregulated DEGs included many chemokine ligands such as CCL8 , CCL2 , CXCL10 , CCL7 , and CXCL11 , and immune signaling associated genes such as P2RX7, STAT1 , and JAK2 ( ; ). Although there appeared to be overall immunosuppressive changes to gene expression in cells infected in ADE compared to conventional infection conditions, select immune signaling molecules were enriched, including top upregulated DEGs CCR7 , ISG15 , IL23A , and TRAF ( ). Pathway analysis was performed using an input of DEGs between infected cells in ADE compared to conventional DENV-2 infection conditions. In infected cells, IPA predicted a predominantly suppressed phenotype in ADE compared to conventionally infected cells ( ; ). Pathway analysis notably indicated downregulation of the inflammatory response in ADE-infected cells, where downregulated pathways included: “neutrophil degranulation,” “pathogen induced cytokine storm signaling pathway,” “interferon gamma signaling,” and “macrophage classical activation signalling pathway” ( ). Pathways involved in leukocyte recruitment, activation, and proliferation were downregulated compared to cells infected in conventional infection conditions. There also appeared to be an enriched transcriptional signature of mitochondrial dysfunction in cells infected in ADE conditions, and downregulated pathways including “electron transport, ATP synthesis and heat production by uncoupling proteins,” and “oxidative phosphorylation” ( ). Of the few upregulated pathways in cells infected in ADE conditions, “IL-12 Signaling and production in macrophages” interestingly emerged as a top pathway. In graphical summary analysis, these processes were summarized as being driven by decreases in cytokine production such as IL-1α, IL-1β, and TNF, and decreased interferon response signaling by STAT1 and JAK2 ( ). These reflect both a decrease in innate anti-viral activation of cytokine responses as well as secondary immune response to IFN production. Together, these processes were highlighted as contributing to downregulated recruitment, activation, and cytotoxicity of leukocytes, IFN-γ, and cytokine storm signaling, as determined by bioinformatic imputation from IPA graphical summary analysis ( ). The observed decrease in transcriptional pro-inflammatory processes in macrophages infected in ADE conditions might indicate mechanisms of attenuated immune response in cells infected by this route of entry which facilitate viral replication and contribute to enhanced viral burden. To confirm that these observations were not driven by misclassification of infected and bystander cells, we also compared the expression profiles of all cells in ADE conditions to all those in DENV-alone conditions. This pseudo-bulk analysis allowed for a more direct comparison of these data to the literature where bulk analysis was performed ( ). Key infection status-specific findings such as decreased chemokine signaling and immune cell recruitment were also identified in bulk analysis ( ). Additionally, this analysis recapitulated published findings which were observed at the bulk level with respect to interferon signaling ( , , ). Pathways specifically identified as downregulated in cells from ADE compared to conventional infection conditions included “antiviral response,” “interferon alpha beta signaling,” and “interferon signaling” ( ). Altogether, bulk analysis of these data reinforces central proposed mechanisms of intrinsic ADE. Beyond this, the discrete differences observed in bulk, compared to cell population-specific analysis, indicate the relevance of additional granularity according to infection status for defining this response. 

Given that infected cells in ADE conditions demonstrated increased mean viral RNA on a per-cell basis, we next sought to determine whether the transcriptomic changes observed in DENV-2-infected cells under ADE conditions were simply due to increased viral burden within cells. To this end, correlation analysis was performed between DENV-2 RNA content and expression of all genes in the data set within the infected cells. While we cannot dismiss the possibility that differing protein content in these cells may correlate with the abundance of host factors, in this analysis, we use the abundance of positive-sense genomic RNA as a proxy for infection burden. For this analysis, Pearson correlation analysis was performed on a per-cell basis to individually compare the expression of each gene identified in the data set to the DENV-2 RNA content in the same cell. Corresponding r 2 values for each of these comparisons were used to determine the correlation strength. Across all host genes captured in this analysis, no significant correlation (all r 2 values <0.6) between DENV-2 (+) RNA expression and the expression of cellular transcripts was observed on a per-cell basis ( ). This observation was also true for the expression of DENV-2 (−) RNA and cellular transcripts. Specific visualization of select differentially expressed genes between infected cells in ADE compared to conventional infection conditions further emphasized the lack of cell-intrinsic correlation between DENV-2 RNA content and differential gene expression. ( ). These findings indicate that transcriptomic differences are likely not driven by differences in DENV-2 RNA content and support that these changes are instead reflective of intrinsic transcriptional differences in ADE and conventional infection. 

Although most of the work defining intrinsic ADE has focused on characterizing either bulk cellular responses or those of infected cells, the potentially unique features of bystander cells in ADE conditions have yet to be explored. We identified discrete differences in the responses of infected and bystander cells across conditions, and therefore postulated that bystander cells in different infection conditions also possessed unique transcriptomic features. We next looked specifically at bystander cells as designated by initial clustering and imputed cell labeling to evaluate gene expression differences between cells in ADE (DENV-2 + IgG) and conventional infection (DENV-2) conditions ( ). In this comparison, DEG analysis identified 467 differentially expressed genes, of which 238 were upregulated and 229 were downregulated ( and ). Notably, these changes along with those identified in pathway analysis indicated that bystander cells harbored more distinct changes between infection conditions than infected cells. Among top upregulated DEGs were TYROBP, ISG15, IFITM3, RPL28, CD74, and CCR7 ( ). Bystander cells in ADE compared to conventional infection conditions uniquely demonstrated significant upregulation of MHC expression. Top downregulated DEGs in bystander cells indicated a decreased enrichment of certain pro-inflammatory cytokines and signaling molecules in ADE compared to conventional infection conditions, including CXCL11, CXCL10, CCL2, DOCK4, STAT1 , and JAK2 ( ). Notably, while many gene expression changes were unique to infected or bystander cells, a number of upregulated or downregulated genes were common between infected and bystander cells in ADE compared to conventional infection conditions. For example, TYROBP, CCR7, CXCL10, and CXCL11 emerged as top DEGs in both infected and bystander cells in ADE compared to conventional infection. In bystander cells, IPA identified enriched pathways including “neutrophil degranulation” and “pathogen induced cytokine storm signaling,” in addition to a number of pathways relating to translation initiation and elongation ( ). Pathway analysis also highlighted a role for antigen presentation pathway upregulation, consistent with the MHC gene expression changes described ( ). Downregulated IL-10 signaling emerged as a top downregulated pathway in bystander cells in ADE conditions, and similar to infected cells, a prominent role for downregulation of metabolic processes involving electron transport and oxidative phosphorylation ( ). Integration of pathway enrichment data in graphical summary demonstrated increased cell viability of phagocytes and APCs, differentiation of lymphocytes, and EIF2 signaling in bystander cells in ADE conditions. These changes were driven by increases in transcript production of various signaling molecules such as HIF1A, EGFR, IL22, IFNE, IFNG, TLR4, IL6, and IL1B ( ). Compared to bystander cells in conventional infection conditions, those in ADE conditions demonstrated an overall activating response by these processes. These findings indicate that in ADE conditions, alternative signaling by infected cells or increased infection burden contributes to distinct transcriptomic differences in bystander cell responses which may provide insight to our understanding of cellular responses to DENV-2 infection. 

In this study, we applied an scRNAseq approach to determine the transcriptomic profiles of infected cells in ADE and conventional infection conditions in order to characterize the cellular response to differing routes of DENV-2 infection. We found that cells infected by ADE, compared to conventional infection, demonstrated a discernible transcriptomic profile characterized by decreased immune signaling, IFN response, and mitochondrial dysfunction. Furthermore, we demonstrate that the observed gene expression and pathway analysis signatures are independent of DENV-2 RNA content, indicating that the observed changes are not simply due to increased viral burden per cell in ADE conditions. We also identify distinct profiles in bystander cells in ADE compared to conventional infection conditions, indicating an activated phenotype with enriched protein translation, cytokine production, and antigen presentation. This work provides novel insight into the cell-intrinsic mechanisms, specifically in infected and bystander cells, which may be responsible for enhanced infection in ADE. The unique profiles of bystander and infected cells within and across conditions highlights the utility and importance of dissecting these profiles in a heterogeneous cell population. The dynamic interplay of virus and host responses is demonstrated by the complex signaling and variable responses of infected and bystander cells to infection. These unique features of infected and bystander cells have been elegantly described with single-cell resolution in a number of pathogens such as influenza, Zika virus (ZIKV), and West Nile Virus ( , ). Our study characterizes the comprehensive transcriptomic profiles of infected and bystander human macrophages in response to DENV-2 infection, contributing to the body of literature describing these divergent responses. Specifically, in the context of intrinsic ADE, most studies investigating cell-intrinsic mechanisms have done so in bulk cultures or have focused on characterizing only the profiles of the infected cells. To our knowledge, we report the first unique characterization of the features of bystander/uninfected cells in ADE conditions. This distinction further lends insights into how bystander cell profiles may contribute to and bias findings when looking at the level of whole cultures. Work by Hamlin et al. in human dendritic cells described an increase in TNF and IL-1β production from DENV-infected compared to bystander cells, which upregulate the expression of ISG IP-10 ( ). We report similar findings in this single-cell analysis of DENV infection of macrophages, where bystander cells took on an anti-viral state, as has been described in other studies in other pathogens ( , , , , ). Hamlin et al. comment that prior studies characterizing infected cell responses by bulk culture analysis indicating cell adoption of an anti-viral state are likely emanating the profiles of the bystander, rather than infected cells in culture ( ). Given the increased magnitude of response that we observe in bystander compared to infected cells and the dominant activating phenotype of these cells, our data also support this observation. We saw IFN-β to be a uniquely upregulated gene in infected cells, consistent with recent findings of Moore et al. in scRNAseq analysis of ZIKV infection, where they report IFN-β expression as a unique feature of infected cells and driving force for the inflammatory pathology in ZIKV infection. Moore et al. identify a strong correlation between ZIKV RNA content and IFNB gene expression ( ); however, we observed no such relationship between DENV RNA content and expression of any genes in our data set. These differences might be due to differences in timing of infection or indicate unique features of IFN response in these two pathogens. Despite the alignment of the expression profiles of the identified cell populations with hallmark features of infected and bystander cells, we acknowledge that it is possible that clustering is driven by alternative factors. It is possible that cells experiencing a low level of infection were captured in the bystander cell group, whereby cell clusters may represent differing timing or stage of infection. However, given the pronounced differences in expression profiles between these populations, their clustering at expected frequencies, and the likely presence of ambient RNA from single-cell isolation, we believe that the inferred infection status applied in this analysis is appropriate. When comparing the gene expression profiles of infected or bystander cells between enhancing and non-enhancing conditions, we identified a number of differentially regulated processes in cells infected in ADE conditions ( ). Previous work has highlighted a potential role for decreased type I and II IFN and pro-inflammatory cytokine responses in ADE-mediated infection ( , , ). Some of this work has very specifically focused on a role for attenuated IFN-β production in cells infected by ADE. Although IFNB did not explicitly emerge as a key DEG between infected cells in ADE compared to conventional infection conditions, we observe a trend toward decreased IFNB transcript production. Similar to previous reports, we identify an overarching signature indicating suppression of immune responses in cells infected by ADE. Our data indicate a likely role for differential cellular response to IFN responses, rather than abrogating innate immune recognition and initial type I IFN induction. It is known that in conventional infection, DENV-2 evades innate immune activation and IFN responses by a number of mechanisms, including its non-structural protein NS3’s inhibition of STAT1 and STAT2 signaling ( ). Given that STAT1 and JAK2 were significantly downregulated in infected cells in ADE compared to conventional infection conditions, it is possible that in ADE-mediated infection, these interactions are differentially modulated such that they further enhance viral evasion strategies. Increased IL-10 production has also been highly proposed as a mechanism of silencing immune activation in ADE conditions or individuals with severe dengue ( , , ). Interestingly, our data demonstrated no significant elevation of IL-10 transcript production in infected or bystander cells in ADE conditions. In fact, in IPA, IL-10 signaling emerged as one of the top downregulated pathways in bystander cells in ADE conditions compared to those in conventional infection. However, it is important to note that many of the previous observations with respect to IL-10 were reported at the protein level, while our findings are examining transcriptomic expression. As with many cytokines, the level of secreted IL-10 may not perfectly correlate with transcript abundance. It has remained difficult to define which proportion of differences in response are due to alternative receptor engagement or differences in virus compartmentalization by route of entry, or are simply driven by differences in viral load in culture. Discordance in the literature regarding mechanisms of intrinsic ADE may be due to the fact that it is impossible to truly distinguish intrinsic from extrinsic features of enhanced infection in realistic physiologic settings. While various methodologies have been employed to control for differences in viral burden, these impact one of the fundamental features of antibody-mediated entry, which is that it elevates virion production and inflammatory pathology. In our analysis, we did not compensate for elevated infection in ADE conditions, because we aimed to evaluate together the intrinsic and extrinsic effects of ADE. We recognize that these findings were confined to a single DENV serotype and, specifically, strain NGC, which has a long passage history, and therefore, determining whether these same associations exist in a more modern DENV strain may be relevant. A major limitation of this work is that it was conducted with cells derived from a single human donor. Although the macrophage differentiation protocol used is believed to mask inter-donor differences, the reproducibility of the observed results in additional donors should be assessed. Finally, this study was conducted at a single time point in an isolated in vitro system that does not allow the assessment of the impact of variable timing of infection and interactions with other immune cells. Future work might seek to understand the functional impact of the observed changes in a less isolated system, determining the effects of different cell phenotypes in other leukocytes, B cells and T cells. Given that this study was conducted with cells derived from a single human donor, additional studies confirming the ability to extend these data more broadly should be performed. It would be meaningful for additional studies to assess whether similar signals can be identified in natural infection or human infection model samples. ADE is a feature of other viruses such as ZIKV, where application of similar methodology would provide useful insight into the underlying intrinsic mechanism and how, and whether these differ in ADE of other pathogens ( ). In this study, we describe evidence of novel pathways and mechanisms which may be driving enhancement in ADE infection conditions. This work provides the groundwork for additional study of these mechanisms, their contribution to ADE pathogenesis, and potential as targets for interventional approaches. 

Viruses DENV-2 (strain Thailand/NGS-C/1944, NCBI taxonomy ID 11065) stocks were prepared by serial passaging in Vero cells. Plaque assay of Vero cells was used to determine stock infectious titer (PFU) for each virus. A multiplicity of infection (MOI) of 1 was used in all infection assays. Monocyte-derived macrophage differentiation Blood from a single healthy donor was collected under SUNY Upstate Medical University IRB approved protocol. PBMCs were then isolated and cryopreserved from this sample, and monocytes were isolated from PBMC and differentiated into macrophages as previously described by Wegman et al ( , ). The Mojosort monocyte isolation kit was used to isolate monocytes by negative selection according to the manufacture’s protocol (Biolegend, #480059) and cells were resuspended in RPMI medium supplemented with 100 ng/mL M-CSF (Peprotech, # 300-25), 10% (vol/vol) fetal bovine serum (FBS, Gibco), 1% penicillin/streptomycin, 1% L-glutamine. Cells were plated on tissue culture-treated 24-well plates at a cell density of 1.25–1.5 × 10 6 cells per milliliter. In order to ensure monocyte adhesion, plates were briefly centrifuged for 2 minutes at 500 g . Day 1 was defined as the day of isolation and plating. Cells were subsequently incubated at 37°C/5% CO 2 with culture media repletion on day 5 by adding 1 mL of differentiation media per well. DENV infection of monocyte-derived macrophages In order to allow immune complex formation, inocula were prepared by incubating DENV-2 with VDB33 monoclonal IgG (ADE condition), DENV-2 alone with media (conventional infection condition), or media alone (uninfected control) at 37 o C/5% CO 2 for 60 minutes. VDB33 is a previously described pan-DENV monoclonal human IgG1 isotype antibody targeting the fusion loop of the DENV E protein. This antibody was isolated from the plasmablast of an individual experiencing a severe DENV-3 infection and has been extensively used by our group to induce ADE in DENV infection of macrophages ( , ). An antibody concentration of 1 µg/mL, previously demonstrated to induce enhancing conditions in primary human macrophages, was used ( , ). On day 6 of culture, supernatants were aspirated from monocyte-derived macrophage cultures and 200 µL of inoculum was added to each well according to desired infection conditions. Cells were incubated with inoculum for 2 hours at 37°C/5% CO 2 , inoculum was removed, and cells were washed twice with 2 mL of RPMI medium. Five hundred milliliters of differentiation media was added to each well and plates incubated for 48 hours at 37°C/5% CO 2 . Macrophages were then detached from plates by adding 600 mL of Accutase (Stemcell Technologies, 07920) per well and incubated at 37°C for 20 minutes. Cells were dissociated by pipetting and transferred to 15 mL conical tubes and analyzed as described. A single replicate of this experiment was performed. Single-cell RNA sequencing Samples were prepared for single-cell RNA sequencing according to the 10x Genomics RNA-seq protocol. Cells were resuspended at a concentration of 500 cells/µL in phosphate-buffered saline (PBS) and loaded for a target of 6,000 cells per reaction. Cells were loaded for Gel emulsion bead (GEM) generation and barcoding. Construction of 5´ gene expression libraries was performed using the Next GEM Single Cell 5´ reagent kit, Library Construction Kit, and the i7 Multiplex Kit (10x Genomics, CA) was used for reverse transcription, complementary DNA amplification, and construction of gene expression libraries. The quality of gene expression libraries was assessed using an Agilent 4200 TapeStation with High Sensitivity D500 ScreenTape Assay and Qubit Fluorometer (ThermoFisher Scientific) according to the manufacturer’s recommendations. Sequencing of 5´ gene expression libraries was performed on an Illumina NextSeq 2000 (Illumina) using P3 reagent kits (100 cycles). Parameters for sequencing were set at 26 cycles for Read1, eight cycles for Index1, and 98 cycles for Read2. 5´ gene expression analysis and visualization The 10x Genomics Cell Ranger pipeline was used to perform gene expression alignment. Sample demultiplexing, alignment, and barcode/unique molecular identifier (UMI) filtering was performed using the Cell Ranger software package (10x Genomics, CA) and bcl2fastq (Illumina, CA) using the commands mkfastq and count. A reference genome was created by combining the human reference genome (Ensembl GRCh38.93) with the DENV-2 genome as an additional chromosome (NC 001474.2). Sequenced transcripts were aligned to a human reference library created using the Cell Ranger mkref command, combined human and DENV-2 reference genome, and custom Ensembl GRCh38 DENV-2 GTF. Reads were mapped to both the positive- and negative-sense DENV2 genome. Quality control metrics for the scRNAseq data set are provided in . Multi-sample integration, data normalization, dimensional reduction, visualization, and differential gene expression were performed in R studio (v.4.3.2) using R package Seurat (v.4.4.0). Sample data sets were filtered to contain cells with less than 22% mitochondrial RNA content and between 400 and 3,500 unique features. Genes expressed in fewer than three cells were excluded from analyses. The resulting data set was normalized and scaled using the Seurat functions NormalizeData(), ScaleData(), and FindVariableFeatures(). Following data normalization and scaling, principal component analysis was performed using RunPCA(). Cells were then clustered based on the first 10 principal components and with a resolution parameter of 0.1 using FindNeighbors() and FindClusters(), respectively. Initial clustering indicated the presence of five clusters, of which two very small but distinct clusters were characterized by B and T cell marker gene expression. We expect that these clusters represent a small number of contaminating cells which remained following monocyte isolation from PBMC. These clusters were thus excluded from subsequent analysis. Statistical analysis Differentially expressed genes were identified by applying the FindMarkers() command and the Wilcoxon rank-sum test to the normalized gene expression data set. A default minimum logFC value of 0.1 and min.pct of 0.01 were used. Adjusted P -values based on Bonferroni correction using all genes in the data set. Genes with a corrected P -value of <0.05 were considered significant. To determine mechanisms associated with gene expression changes, IPA (Qiagen) was performed. Analysis was performed using genes with a corrected P -value <0.01 and log fold change <−0.25 or >0.25. IPA results with a P -value <0.01 and z -score <−0.25 or >0.25 were considered statistically significant. 

DENV-2 (strain Thailand/NGS-C/1944, NCBI taxonomy ID 11065) stocks were prepared by serial passaging in Vero cells. Plaque assay of Vero cells was used to determine stock infectious titer (PFU) for each virus. A multiplicity of infection (MOI) of 1 was used in all infection assays. 

Blood from a single healthy donor was collected under SUNY Upstate Medical University IRB approved protocol. PBMCs were then isolated and cryopreserved from this sample, and monocytes were isolated from PBMC and differentiated into macrophages as previously described by Wegman et al ( , ). The Mojosort monocyte isolation kit was used to isolate monocytes by negative selection according to the manufacture’s protocol (Biolegend, #480059) and cells were resuspended in RPMI medium supplemented with 100 ng/mL M-CSF (Peprotech, # 300-25), 10% (vol/vol) fetal bovine serum (FBS, Gibco), 1% penicillin/streptomycin, 1% L-glutamine. Cells were plated on tissue culture-treated 24-well plates at a cell density of 1.25–1.5 × 10 6 cells per milliliter. In order to ensure monocyte adhesion, plates were briefly centrifuged for 2 minutes at 500 g . Day 1 was defined as the day of isolation and plating. Cells were subsequently incubated at 37°C/5% CO 2 with culture media repletion on day 5 by adding 1 mL of differentiation media per well. 

In order to allow immune complex formation, inocula were prepared by incubating DENV-2 with VDB33 monoclonal IgG (ADE condition), DENV-2 alone with media (conventional infection condition), or media alone (uninfected control) at 37 o C/5% CO 2 for 60 minutes. VDB33 is a previously described pan-DENV monoclonal human IgG1 isotype antibody targeting the fusion loop of the DENV E protein. This antibody was isolated from the plasmablast of an individual experiencing a severe DENV-3 infection and has been extensively used by our group to induce ADE in DENV infection of macrophages ( , ). An antibody concentration of 1 µg/mL, previously demonstrated to induce enhancing conditions in primary human macrophages, was used ( , ). On day 6 of culture, supernatants were aspirated from monocyte-derived macrophage cultures and 200 µL of inoculum was added to each well according to desired infection conditions. Cells were incubated with inoculum for 2 hours at 37°C/5% CO 2 , inoculum was removed, and cells were washed twice with 2 mL of RPMI medium. Five hundred milliliters of differentiation media was added to each well and plates incubated for 48 hours at 37°C/5% CO 2 . Macrophages were then detached from plates by adding 600 mL of Accutase (Stemcell Technologies, 07920) per well and incubated at 37°C for 20 minutes. Cells were dissociated by pipetting and transferred to 15 mL conical tubes and analyzed as described. A single replicate of this experiment was performed. 

Samples were prepared for single-cell RNA sequencing according to the 10x Genomics RNA-seq protocol. Cells were resuspended at a concentration of 500 cells/µL in phosphate-buffered saline (PBS) and loaded for a target of 6,000 cells per reaction. Cells were loaded for Gel emulsion bead (GEM) generation and barcoding. Construction of 5´ gene expression libraries was performed using the Next GEM Single Cell 5´ reagent kit, Library Construction Kit, and the i7 Multiplex Kit (10x Genomics, CA) was used for reverse transcription, complementary DNA amplification, and construction of gene expression libraries. The quality of gene expression libraries was assessed using an Agilent 4200 TapeStation with High Sensitivity D500 ScreenTape Assay and Qubit Fluorometer (ThermoFisher Scientific) according to the manufacturer’s recommendations. Sequencing of 5´ gene expression libraries was performed on an Illumina NextSeq 2000 (Illumina) using P3 reagent kits (100 cycles). Parameters for sequencing were set at 26 cycles for Read1, eight cycles for Index1, and 98 cycles for Read2. 

The 10x Genomics Cell Ranger pipeline was used to perform gene expression alignment. Sample demultiplexing, alignment, and barcode/unique molecular identifier (UMI) filtering was performed using the Cell Ranger software package (10x Genomics, CA) and bcl2fastq (Illumina, CA) using the commands mkfastq and count. A reference genome was created by combining the human reference genome (Ensembl GRCh38.93) with the DENV-2 genome as an additional chromosome (NC 001474.2). Sequenced transcripts were aligned to a human reference library created using the Cell Ranger mkref command, combined human and DENV-2 reference genome, and custom Ensembl GRCh38 DENV-2 GTF. Reads were mapped to both the positive- and negative-sense DENV2 genome. Quality control metrics for the scRNAseq data set are provided in . Multi-sample integration, data normalization, dimensional reduction, visualization, and differential gene expression were performed in R studio (v.4.3.2) using R package Seurat (v.4.4.0). Sample data sets were filtered to contain cells with less than 22% mitochondrial RNA content and between 400 and 3,500 unique features. Genes expressed in fewer than three cells were excluded from analyses. The resulting data set was normalized and scaled using the Seurat functions NormalizeData(), ScaleData(), and FindVariableFeatures(). Following data normalization and scaling, principal component analysis was performed using RunPCA(). Cells were then clustered based on the first 10 principal components and with a resolution parameter of 0.1 using FindNeighbors() and FindClusters(), respectively. Initial clustering indicated the presence of five clusters, of which two very small but distinct clusters were characterized by B and T cell marker gene expression. We expect that these clusters represent a small number of contaminating cells which remained following monocyte isolation from PBMC. These clusters were thus excluded from subsequent analysis. 

Differentially expressed genes were identified by applying the FindMarkers() command and the Wilcoxon rank-sum test to the normalized gene expression data set. A default minimum logFC value of 0.1 and min.pct of 0.01 were used. Adjusted P -values based on Bonferroni correction using all genes in the data set. Genes with a corrected P -value of <0.05 were considered significant. To determine mechanisms associated with gene expression changes, IPA (Qiagen) was performed. Analysis was performed using genes with a corrected P -value <0.01 and log fold change <−0.25 or >0.25. IPA results with a P -value <0.01 and z -score <−0.25 or >0.25 were considered statistically significant. 

