Distributions of JEV in brain regions of infected mouse

To establish the JEV-infected mouse model and understand the viral tropism in brain regions, 5–6-week-old C57BL/6 mice were intraperitoneally injected with 105 PFU JEV or equal volume of Dulbecco’s modified Eagle’s medium (DMEM). A significant reduction of body weight and obvious clinical symptoms were observed in mice after JEV infection (Additional file 1: Fig. S1A). Interestingly, the JEV-infected mice developed different symptoms during our observation. At 7 days post infection (dpi), some mice only showed mild symptoms, such as ruffled fur or hindlimb weakness, while some mice appeared severe symptoms like quiver and paralysis (Additional file 1: Fig. S1B, C). The brain tissues of mice exhibiting different symptoms were isolated at 7 dpi. The viral loads and the infected regions in the brain were determined by plaque assay and immunohistochemistry (IHC) assay respectively. An elevation on viral titers was observed in the brains of mice displaying severe symptoms, as compared to those in mice with mild symptoms (Additional file 1: Fig. S1D). In addition, the IHC analysis with the sagittal and coronal brain slice revealed that JEV was predominantly present in the cortex, striatum, and thalamus. As symptoms worsened, an increase in the abundance of viral protein in the brain was detected (Additional file 1: Fig. S1E, F).

Single-cell transcriptional profiling of JEV-infected mouse brain

To captures the transcriptional landscape of various cell types in the brain tissue of mice developing different symptoms following JEV infection, the scRNA-seq (10 × Genomic) technology was employed (Fig. 1A). 5–6-week-old C57BL/6 mice were intraperitoneally with JEV (JEV-infected group, n = 5) or DMEM (mock-infected group, n = 3), and the body weight of mice was recorded daily for 15 consecutive days (Additional file 2: Fig. S2A). At 7 dpi, 3 mice presented mild symptoms while 2 mice displayed severe symptoms. The single cells were isolated from the cortex, striatum and thalamus areas of the brain in JEV-infected mice with different symptoms, as well as mock-infected mice, on 8 corona slices (Additional file 2: Fig. S2B). Single cell suspension was then visualized under a microscope, and the cell viability was examined. The results showed that we successfully obtained a sufficient number of single cells with over 90% viability (Additional file 2: Fig. S2C, D), and copies of JEV E gene in the single cell was measured (Additional file 2: Fig. S2E).

Fig. 1

Changes of cell types upon JEV infection in mouse brain revealed by scRNA-Seq. A Schematic diagram showing the isolation of single cells from cerebral cortex, striatum and thalamus of mock- and JEV-infected mice for scRNA-seq. B Overview of the cell types in the integrated single-cell transcriptomes of 88,000 cells derived from mock- and JEV-infected mouse brain. C tSNE plots of cell types from different symptoms. D Proportion of different cell types in mice with different symptoms. E tSNE plot colored for gene expression in different cell types. F The marker genes used to define each cell type are illustrated in the violin plot

After performing scRNA-seq, we obtained a total of 88,000 transcriptomes from single cells in the brain tissues of 3 mock-infected mice and 5 JEV-infected mice exhibiting mild and severe symptoms. By analyzing the top differentially expressed genes (DEGs) (Additional file 3: Fig. S3B), we identified 34 clusters (Additional file 3: Fig. S3A–C) and 10 major cell types (Fig. 1B–D). These included astrocytes (Ast) (Gia1+), endothelial cells (End) (Flt1 + Cldn5 +), microglia & macrophages (Mic_Mac) (Tmem119 + Cx3cr1 + Aif1 +), monocytes & macrophages (Mon_Mac) (CD14 + Ly6c2 +), mural cells (Mur) (Vtn + Pdgfrb +), neurons (Snap25 + Syt1 + Tubb3 +), natural killer (NK) cells (Nkg7 + Ccl5 +), oligodendrocytes (OLG) (Plp1 + Mbp +), oligodendrocyte precursor cells (OPCs) (Olig1 + Olig2 +), and T cells (CD3g + CD3g + CD3e +) (Fig. 1C–F). Our results revealed that as the symptoms worsened following JEV infection, there was a progressive increase in immune-related cells, whereas the population of neurons and glial cells gradually declined in mouse brain (Additional file 3: Fig. S3D, E). In the group with mild symptoms, Mon_Mac, T and NK cells started to emerge, and a new cluster of mural cells and endothelial cells appeared. Additionally, a new cluster of OPCs, OLG and Mic_Mac emerged. In brains of mice with severe symptoms, Mon_Mac accounted for more than half of the cells, and a notable reduction in the number of neurons and glial cells was observed (Fig. 1C, D).

To validate the scRNA-seq results, flow cytometry was performed to detect the presence of Mic_Mac, Mon_Mac, T cells, and NK cells. As the severity of symptoms increased, the proportion of Mic_Mac (CD11b + CD45low) among total viable cells was decreased. Additionally, the ratio of Cx3cr1-positive cells, which are known as resting microglia cells, was reduced among Mic_Mac, indicating that most Mic_Mac were activated (Fig. 2A, B). In consistent with the scRNA-seq results, Mon_Mac (CD11b + CD45high) showed a dramatical increase, accompanied by a notable rise in the number of cells expressing the major histocompatibility complex class II (MHC-II), particularly in mice exhibiting severe symptoms (Fig. 2C, D). Similarly, the proportion of NK cells, identified using the CD45+ CD3−CD49+ approach, also showed a marked elevation (Fig. 2E, F). Furthermore, we identified T cells by labeling CD4+ T cells as CD45+ CD3+ CD4+ and CD8+ T cells as CD45+ CD3+ CD8a+. We observed a significant increase in total number of T cells after JEV infection. Notably, the CD4+ T cells were found to be more enriched than CD8+ T cells (Fig. 2F, G) [45, 46]. To further confirm these findings, the IHC assay was performed, and consistent changes on Mon_Mac, Mic_Mac, T cells and NK cells were observed (Additional file 4: Fig. S4). Taken together, these results suggest that after JEV infection, most Mic_Mac in the brain were activated, and the immune cells such as Mon_Mac, NK and T cells distributed near the blood vessels were significantly increased.

Fig. 2

Flow cytometry analysis of immune cells in brains of mice with different symptoms upon JEV infection. A, C, E, G Flow cytometry gating strategy of Mic_Mac (A), Mon_Mac (C), NK cells (E) and T cells (G). B Percentage of CD11b + CD45low cells in total living cells and percentage of Cx3cr1 + cells in Mic_Mac cells. D Percentage of CD11b + CD45high cells in total living cells and the number of cells that secrete MHC-II. F Percentage of CD49+ CD3-CD45+ cells in total living cells. H Cell numbers of CD3+, CD4+ CD3+, and CD8a + CD3+ cells. Data from 3 independent experiments were presented as mean ± SEM. The statistics were analyzed using two-tailed Student’s t-test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. ns: nonsignificant

Dynamic composition and functional changes of brain cells during JEV infection

The scRNA-seq results showed significant alterations in cell composition and gene expression among different groups. As symptoms worsen, an increasing amount of differentially expressed genes was observed (Fig. 3A). The Venn diagram revealed that the intersection of the upregulated genes between mice with mild symptoms compared to mock-infected mice and mice with severe symptoms compared to mock-infected mice are associated with cytoplasmic translation and immune system process, while the down-regulated genes are related to the plasma membrane (Fig. 3B). In addition, as the symptoms worsened, the number of newly differentiated genes gradually increased, among which the upregulated genes were found to be related to autophagy and NF-κB pathways, while the downregulated genes are associated with nervous system development (Fig. 3B, C). Furthermore, compared with the mock-infected group, the top 20 KEGG enrichments were related to antigen processing and presentation in mild symptoms (Fig. 3D). While in severe symptoms, differentially regulated genes were predominantly enriched in cell adhesion molecules, phagosomes, and the chemokine signaling pathway (Fig. 3E).

Fig. 3

Changes of gene expression in JEV-infected mouse brain. A The top 5 significantly altered genes (rows) in each group. B, C Venn diagram and GO analysis of overlapping up- (B) and down-regulated (C) genes across different symptoms. D, E Analysis of the top 20 KEGG pathways

In order to understand the responses of different cells in mouse brain to viral infection, further analysis was conducted to compare the differentially regulated genes between the JEV-infected and mock-infected groups. It was observed that common upregulated genes were shared across different cell types (Additional file 5: Fig. S5), indicating a general response to the virus. These upregulated genes are involved in various processes such as response to interferon, cytokine production, cytosolic ribosome, and negative regulation of the immune system. In contrast, the downregulated genes showed cell-type specific changes. For example, the function of downregulated genes in endothelial cells is associated with vasculature development. Similarly, downregulated genes in mural cells regulate cell adhesion, while downregulated genes in neurons are involved in myelination. Additionally, downregulated genes in oligodendrocytes and oligodendrocyte precursor cells are linked to axon.

Cell–cell communications in JEV-infected mouse brain

To further understand the communications between different types of cells in the brain tissues of mice during JEV infection, Ligand-receptor analysis and visualization were performed using CellChat version 1.6.0. A higher number of inferred interactions and the interaction strength was found in JEV-infected group compared to that in the mock-infected group (Fig. 4A, B). Among the interactions, T cells showed the highest frequency and strength of interactions with other cells, particularly with endothelial cells, Mic_Mac, and NK cells (Fig. 4A, B). Then, we conducted a communication pattern analysis in both mock- (Fig. 4C) and JEV-infected groups (Fig. 4D), revealing the two patterns in outgoing secreting cells and three patterns in incoming target cells. Pattern 1 was identified in outgoing Mic_Mac, NK and T cells signaling, involving multiple pathways, such as chemokine (C-C motif) ligand (CCL), GALECTIN, colony stimulating factor (CSF), tumor necrosis factor (TNF), interleukin 2 (IL2) and FASLG. In JEV-infected group, new pathways such as CXCL, secreted phosphoprotein 1 (SPP1), protease activated receptors (PARS) and oncostatin-M (OSM) emerged, contributing to inflammation, immune responses and cell proliferation or differentiation. Outgoing astrocyte, endothelial cells, mural, neuron, oligodendrocytes, and oligodendrocyte precursor cells signaling was identified in pattern 2. Both the mock- and JEV-infected groups were enriched in pleiotrophin (PTN), prosaposin (PSAP), Midkine (MK), and CX3C, which are involved in information transmission and neuronal repair functions. In addition, there is a significant difference in the incoming communication patterns between mock- and JEV-infected groups. In mock-infected group, both endothelial cells and neurons showed consistent pattern. However, in JEV-infected groups, endothelial cells presented a separate pattern. This separate pattern represents pathways such as Protease Activated Receptors (PARs), OSM, KIT, angiopoietin (ANGPT), and tumor necrosis factor-like weak inducer of apoptosis (TWEAK), which are associated with inflammation, vascular regeneration, and regulate cell growth and development. Neuron, astrocyte, mural, oligodendrocytes and oligodendrocyte precursor cells were identified as part of a different pattern that represents the endothelin (EDN) pathway, whereas Mic_Mac, Mon_Mac, NK, and T cells were found to be associated with pattern3, which is linked to the TGFb and IFN-II pathways.

Fig. 4

Cell–cell communications in JEV-infected mouse brain. A, B Circle plot showing the number of interaction (A) and interaction strength (B) of different cells in brain. The line thickness represents the number and the strength of signaling. The red lines represent JEV-infected mouse brain and blue lines represent the mock-infected mouse brain. Histogram shows interactions number and strength interaction. C, D River plot showing outgoing communication pattern of secreting cells and incoming communication pattern of target cells in mock-infected group (C) and JEV-infected group (D). E Information flow of each signaling pathway from cell–cell interaction analysis. The receptor-ligand pathways with blue text are significantly enriched in JEV-infected brain cells, and pathways with red text are significantly enriched in mock-infected mouse brain cells. F, G Number of neuronal cells communicating with other cells as ligands in mock-infected group (F) and JEV-infected group (G). H, K Bubble plots show the variable ligand-receptor pairs between neuronal cells and other cells, when the neurons are ligands (H) or receptors (F). I, J Number of neuronal cells communicating with other cells as receptors in mock-infected group (I) and JEV-infected group (J)

In addition, we investigated the signaling pathways potentially involved in cell communications in mock- and JEV-infected groups. We found that laminin and neural cell adhesion molecule (NCAM) signaling, which play a crucial role in regulating cell growth and differentiation, were particularly activated in mock-infected group (Fig. 4E). On the other hand, the JEV-infected group showed upregulation of claudin (CLDN, a vital component of epithelial cell tight junctions), CCL (related to inflammation), Galectin (involved in regulating key biological processes including cell growth, differentiation, apoptosis, and immune responses), and MHC-I (primarily responsible for mediating the cytotoxic effect of T cells) signaling pathways (Fig. 4E). These results suggest that JEV infections not only result in neuronal damage and disruption of the cellular barrier, but also trigger the body’s immune response and secretion of various cytokines to combat the viral infections.

As neurons have been identified as the primary target cells of JEV in the CNS, studying their interactions with other cells is crucial for understanding JEV pathogenesis. Interestingly, we observed that when neurons act as ligands, neuron presented the most frequent interactions with OPCs, both in mock- (Fig. 4F) and JEV-infected groups (Fig. 4G), the Ptn secreted by neurons primarily interacts with Sdc1/2/3 and Ptprz (Protein Tyrosine Phosphatase, Receptor Type Z) on the surface of OPCs, thereby regulating neuronal growth and development (Fig. 4H). Additionally, H2-t23, H2-t22, H2-k1, and H2-d1 secreted by neurons could interact with Cd8a and Cd8b1 on the surface of T cells. Moreover, the neurons were also predicted to communicate with other immune cells. For instance, H2-t23 from neurons was shown to interact with Kldr1 + Klrc2 and Kldr1 + Klrc1 on the surface of NK cells, while Mif and App interact with Cd74 on Mic_Mac and Mon_Mac cells (Fig. 4H). When the neuron acts as a receptor, OLG and OPCs presented the most frequent interactions with neurons, both in mock- (Fig. 4I) and JEV-infected groups (Fig. 4J). Upon JEV-infection, the expression of Ncl, which receives Ptn signal from glial cells and mural cells, was upregulated in neurons, while Ncam1 and Cldn11, which are involved in cell adhesion and tight junction, were found to be downregulated in neurons (Fig. 4K). The findings indicate that JEV infection in neurons triggers the secretion of certain molecules by glial cells, which in turn promote neuronal growth and development while decreasing neuronal adhesion. Additionally, neurons engage in interactions with immune-related cells to facilitate the clearance of the virus.

Heterogeneous subpopulations of immune cells in JEV-infected mouse brain

Considering the notable variations in the composition of immune related cells in mouse brains of different groups (Fig. 5A), we conducted the subclass analysis on these cells to understand the involvement of different subpopulations of immune cells in JEV infection. Mic_Mac cells, which are major immune cells in the brain, have been reported to play a crucial role in the progression of JEV infection. In this study, we identified six clusters of Mic_Mac cells (Additional file 6: Fig. S6A). Clusters 2 and 4 were primarily found in the brains of mock infected group, while cluster 1 was identified in both mock-infected and mildly symptomatic groups. Cluster 5 was predominantly observed in the brains of mice with mild symptoms, whereas clusters 3 and 6 were mostly enriched in those with severe symptoms (Additional file 6: Fig. S6B, C). By comparing the gene expression from different clusters, we found that clusters 1, 2, and 4 shared similar gene profiles related to anti-inflammatory M2 type macrophages. Based on the highly expressed genes in these cells, we designated these cells as Mic1-p2ry12 (Fig. 5B). On the other hand, clusters 3, 5, and 6 displayed a more M1 type-dominant gene signature, such as the high expression of Ccl2, Isg15 and Ifi211 (Additional file 6: Fig. S6C). These clusters of cells were named Mic2-Il1b, Mic3-Hbb-bs and Mic4-Apod, respectively, based on their differentially expressed genes (Fig. 5B, C). Conversely, clusters 1, 2, 4, and 5 exhibited high expression levels of Tmem119 and Cx3cr1, suggesting a resting state of microglia. Interestingly, clusters 3 and 6 showed increased expression of Aif1 (iba1) after JEV infection, indicating a transformation of microglia from a resting state to an active state (Additional file 6: Fig. S6C).

Fig. 5

Subclusters of immune cells and the potential developmental trajectory of cell subsets. A tSNE visualization of immune related cell populations in different group. B, D, F, H Overview of the subclusters of Mic_Mon (B), Mon_Mic (D), T (F), and NK (H) cells. The subclusters were named based on the cluster-specific gene expression patterns. C, E, G, I Heatmap of differentially regulated genes among subtypes of Mic_Mon (C), Mon_Mic (E), T (G), and NK (I) cells

To investigate the evolutionary trajectory of Mic_Mac cells, we conducted a pseudo-time analysis using Monocle 2. In this analysis, we observed a similar phenomenon where Mic_Mac cells from mock-infected mice were predominantly found at the beginning of the trajectory (Additional file 6: Fig. S6D, E). However, the trajectory of Mic_Mac cells from mice with mild symptoms deviated from that of mock-infected mice, while the opposite end of the trajectory was comprised of Mic_Mac cells from mice with severe symptoms. Furthermore, we also analyzed the cells at the branch point, both before and after differentiation. We generated a heatmap of the differentially enriched genes, allowing the identification of four modules. Afterwards, we performed individual GO enrichment and KEGG analysis on these modules (Additional file 6: Fig. S6F). The analysis of module 1 and 2 revealed that the developmental track of Mic_Mac cells in cell fate 1 primarily involved inflammation responses, cytokine activity, and viral protein interaction with cytokine and cytokine receptor. In cell fate 2, it was mainly associated with the chemokine-mediated signaling pathway.

Mon_Mac cells, as the most diverse cell population after virus infection, exhibited notable heterogeneity with distinct clusters (Additional file 7: Fig. S7A, B). These subclusters were named based on their gene expression patterns (Fig. 5D). In the Mon1-Gpnmb cluster, the complement system genes C1qb and C1qc were significantly up-regulated (Fig. 5E), indicating their involvement in apoptosis. The Mon2-Bcl2a1a cluster showed high expression of Il1b, Bcl2a1a, and Ly6i, which are associated with the NF-κB signaling pathway. In addition, the Mon3-Slpi and Mon4-Vcan clusters were found to be associated with inflammation, while the Mon5-H2-Aa cluster exhibited expression of H2-Eb1, H2-Aa, and Ccr7, indicating its involvement in antigen presentation (Additional file 7: Fig. S7C). Furthermore, single-cell trajectory analysis identified clusters 4 and 5 as the earliest cells to emerge during disease development (Additional file 7: Fig. S7D). Functional enrichment analysis suggested that these clusters were associated with inflammatory response and antigen processing and presentation. Subsequently, cluster 3 emerged, characterized by genes involved in negative regulation of viral replication. On the other end of the trajectory, clusters 1 and 2 were enriched with genes participating in the mTOR signaling pathway and NK cell-mediated cytotoxicity (Additional file 7: Fig. S7D–F).

A total of 3320 T cells were detected in all samples and classified into 9 clusters using t-SNE. The majority of cells were derived from individuals with mild and severe symptoms (Additional file 8: Fig. S8A, B). Based on the expression of marker genes of specific T cell subtypes, 2 clusters were identified as CD4+ T cells (20.66%), namely CD4-C1-Tnfrsf4 and CD4-C2-Il1b. 7 clusters were identified as CD8+ T cells (79.34%), including the CD8-C1-Prf1 cluster, associated with cytotoxic T cells, the CD8-C2-S100a4 cluster, representing effector memory T cells, and the CD8-C3-Mki67 cluster, known as proliferative T cells (Fig. 5F and G).We further investigated the dynamic immune states of T cells using Monocle. The cells were distributed on a trajectory chart consisting of three branches (Additional file 8: Fig. S8D, E). We subsequently analyzed the expression patterns of all identified genes during the progression of T cell exhaustion. As a result, we identified four modules by generating a heatmap of the differentially enriched genes. The CD4-C2-Il1b and CD8-C2-S100a4 cells, belonging to module 3 were observed at the starting point of the pseudotime (Additional file 8: Fig. S8E). GO analysis indicated that their function is mainly associated with defense response to viruses and response to interferon-beta (Additional file 8: Fig. S8E). Cell fate 1 was shown at the upper left of the branch point, which is enriched with CD8-C1-Prf1 and CD4-C1-Tnfrsf4. The genes enriched in this module are associated with viral protein interaction with cytokines and cytokine receptors. The other end of the trajectory is populated with CD8-C2-S100a4 cells, which are mainly related to natural killer cell-mediated cytotoxicity and adaptive immune response (Additional file 8: Fig. S8F).

The number of NK cells was relatively low, with a count of 842 cells, and the majority of these cells were derived from JEV-infected mice (Additional file 9: Fig. S9A–C). In this study, NK cells were divided into three subpopulations based on their expression of Ly6a, Ncr1, and Xcl1, instead of the traditional classification into CD56bright and CD56dim subpopulations (Fig. 5H, I). Pseudotime analysis revealed that NK cells from each of the 3 clusters were assigned to the 3 different developmental trajectories (Additional file 9: Fig. S9D). It was found that Ncr1 + NK cells were in the early stage of the developmental trajectory (Additional file 9: Fig. S9E). Along cell fate 1, NK cells develop with a focus on antiviral activity and cytotoxicity. Along cell fate 2, they play a role in immune regulatory functions by regulating cytokine interactions and T cell proliferation (Additional file 9: Fig. S9F).

The cell types susceptible to JEV in the CNS

Although neurons have long been recognized as the primary target cells of JEV in the CNS, the specific subtype of neurons that JEV prefers and the susceptibility of other cell types to JEV remain uncertain. Our analysis of the scRNA-seq data revealed that neurons exhibit the highest level of infection (Fig. 6A), which aligns with previous findings. However, we also observed that other cells in the brain, such as glia cells, mural cells, and infiltrating cells, can also be infected by JEV (Fig. 6B). Flow cytometry analysis verified these findings, indicating a higher proportion of infected neuronal cells as the symptoms become more severe (Fig. 6C, D).

Fig. 6

Baiap2 positive neurons exhibit high susceptibility to JEV. A Distribution of JEV genome in different cells. B Percentage of JEV-infected cells (the count of JEV gene > 500) in each cell type. C Flow cytometry for detection of JEV E protein and NeuN in brain cells of mice infected with JEV. D The histograms summarizing the indicated cell populations of JEV-infected neurons (n = 4). E tSNE visualization of clustering revealed 15 distinct neuronal populations. F Histogram showing the proportions of cells in each group and each subcluster. G Dot plot showing the expression of representative markers that define the cluster in neurons. H tSNE visualization of JEV gene expression in neuron cells and heatmap of JEV transcript positive cells separated by different symptoms. I Heatmap showing the neurons with different abundance of JEV genomic RNA. J The top 5 genes in brain cells infected with different amount of JEV. Different color dots represent viral gene expression, yellow dot: low expression (Log2 Exp JEV less than 3); green dot: medium expression (Log2 Exp JEV between 3 and 8); red dot: high expression (Log2 Exp JEV more than 8). K Heatmap of Baiap2 expression in neurons in mouse brain. L Flow cytometry analysis of JEV and Baiap2 positive cells in brains of mice with different symptoms after JEV infection. M The histograms summarizing the ratios of indicated cells infected with JEV in the Baiap2 positive neurons (n = 4). N The histograms summarizing the ratio of Baiap2 and JEV positive cells in NeuN and JEV positive cells. Data from 3 independent experiments were presented as mean ± SEM. The statistics were analyzed using two-tailed Student’s t-test. ns: nonsignificant

Given the high prevalence of infection in neurons, we further investigated the susceptibility of different subtypes of neurons to JEV. We performed subcluster on a total of 27,874 neurons, resulting in 15 subsets (Fig. 6E). After viral infection, subclusters 11, 12, and 15 emerged, while the remaining 12 clusters were present both before and after infection (Fig. 6F). Notably, each cluster exhibited specifical expression of neuron-related markers (Fig. 6G). Furthermore, we analyzed the expression of viral genes in different neuronal subtypes and found that it was not confined to a particular subgroup. Instead it was expressed in multiple subgroups, showing noticeable differences in expression levels (Fig. 6H). As the classification of neurons into five types, namely glutamatergic, aminobutyric acid, dopaminergic, serotoninergic, and cholinergic neurons, is well-established based on their secretion of neurotransmitters, we initially analyzed the susceptibility of these neuronal types to JEV according to the scRNA-seq results. Our findings revealed that both glutamatergic and aminobutyric acid neurons were susceptible to JEV infection (Additional file 10: Fig. S10). However, dopaminergic and serotoninergic neurons were not detected in the scRNA-seq results. To further determine the subtype of neuron that JEV prefers most, we classified neurons into three groups based on the level of viral gene expression: low expression (Log2 Exp JEV less than 3), medium expression (Log2 Exp JEV between 3 and 8), and high expression (Log2 Exp JEV more than 8) (Fig. 6I). By analyzing differentially expressed genes, we found a strong correlation between the level of Baiap2 expression and JEV infection (Fig. 6J, K). The presence of expression of Baiap2 and viral genes in the brain tissues of both JEV-infected and mock-infected mice indicates that the increased expression of Baiap2 was not attributed to JEV infection. By conducting flow cytometry, we found as symptoms worsen, the proportion of Baiap2-positive (Baiap2+) cells in all JEV positive cells increased (Fig. 6L, M). Furthermore, compared with severe symptoms, mild symptoms have a higher proportion of Baiap2 and JEV positive cells in all NeuN and JEV positive cells (Fig. 6N), which supports that the tropism of JEV to Baiap2-positive cells is not caused by the upregulation of Baiap2 upon JEV infection.

To further verify the correlation between Baiap2 expression and JEV infection, we performed the fluorescence in situ hybridization (FISH) and IHC assays. The results of FISH assay with brain tissue sections showed that more viral RNA was detected in Baiap2 mRNA positive neurons (Fig. 7A, B). Similarly, the IHC assay revealed that more JEV E protein was observed in Baiap2 + neurons, and the proportion of JEV E positive cells in Baiap2 + neurons increased with worsening of symptoms (Fig. 7C, D). These results suggest that JEV predominantly infected Baiap2 + neurons (Fig. 7C, D).

Fig. 7

The expression of JEV genes is positively correlated with the expression of Baiap2 in neurons. A Detection of JEV, Rbfox3 and Baiap2 mRNA in brain coronal sections of mice with different symptoms by fluorescence in situ hybridization. B Enlarged view of area in A. (slice = 10 μm, Scale bars = 500 μm). (C) Detection of the expression of JEV E protein, Baiap2 and NeuN in mouse brain. (slice = 5 μm, Scale bars = 200 μm). D Statistical analysis of percentage of Baiap2 and JEV positive cells in NeuN and JEV positive cells in brain sections. Data from 3 slices were presented as mean ± SEM. The statistics were analyzed using two-tailed Student’s t-test. *P < 0.05, ***P < 0.001, ****P < 0.0001. ns: nonsignificant