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3 February 2026

19 Pages

Single-Cell Sequencing Reveals the Immune Characteristics of Secondary Liver Injury Induced Indirectly by CHIKV Infection in Rhesus Macaques

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1
Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College (IMBCAMS&PUMC), 935 Jiaoling Road, Kunming 650118, China
2
State Key Laboratory of Respiratory Health and Multimorbidity, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
3
Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
4
Yunnan Key Laboratory of Cross-Border Infectious Disease Control and Prevention and Novel Drug Development, Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming 650118, China
Viruses2026, 18(2), 201;https://doi.org/10.3390/v18020201
This article belongs to the Special Issue Chikungunya Virus in Viral Immunology and Vaccine Research
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Abstract

Chikungunya virus (CHIKV) is now prevalent in multiple regions worldwide, posing a serious threat to human health. In this study, we have successfully established a rhesus macaque model of Chikungunya virus infection. Notably, while no viral load was detected in liver tissue on day 7 post-infection, significant pathological damage was observed. Single-cell RNA sequencing of liver tissue revealed a reduction in B cells following infection. Among T cells, CD8+ T and NKT cells mediated major cytotoxic effects, whereas CD4+ T and memory T cells primarily exerted regulatory functions that further enhanced the activation of CD8+ T and NKT cells. In macrophages, inflammatory macrophages fc gamma R-mediated phagocytosis upregulated, with multiple key activation-related genes being highly upregulated. Furthermore, we observed that there might be a potential bidirectional activation effect between T cells and macrophages. These results indicate that CHIKV-induced indirect liver injury is likely mediated not only by the virus itself but also, in part, by the activation of hepatic immune cells.

1. Introduction

Chikungunya virus (CHIKV) is an alphavirus in the family Togaviridae, first isolated from the serum of a patient in Tanzania in 1952 [1]. Initially, CHIKV only erupted occasionally in Africa and Asia, but with the expansion of human activities, CHIKV has spread over the world and caused major outbreaks multiple times [2]. Although CHIKV infection has a low fatality rate, a high incidence, and usually a significant impact on the quality of life of the infected, causing substantial economic loss. For the vast majority of patients, the infection is characterized by fever and is always accompanied by arthralgia. It also includes some mild polymyalgia and arthritis, rash, myalgia, headache, etc. [3]. CHIKV infection is generally a self-limited disease, but some patients may experience persistent joint pain; after the acute phase of the disease, it may last for months or even years [4].
CHIKV has a single-stranded, positive-sense RNA genome approximately 12 kb in length. This genome encodes two polyproteins that are cleaved into four non-structural proteins (nsP1-4) and five structural proteins (C, E3, E2, 6K, and E1). On the surface of the viral particle, the heterodimeric trimerization of structural proteins E1 and E2 forms the “viral spike”; Glycoprotein E2 functions by binding to the receptor, while E1 functions by membrane fusion [5]. Although alphavirus enters host cells by clathrin-mediated endocytosis [6], multiple attachment factors and potential receptors for CHIKV entry are critical. The known mammalian cell receptors for CHIKV include pleckstrin homology domain-containing protein (PHB), phosphatidylserine TIM-1, MXRA8, CD147 protein complex, C-type calcium-dependent lectin DC-SIGN (DC-specific cell adhesion molecule--grabbing nonintegrin) [7,8,9,10,11,12]. In mosquitoes, ATP synthase beta subunit has been reported as a receptor for CHIKV [13]. Moreover, other phosphodiesterase-binding proteins, such as Axl and TIM-4, have also been shown to promote CHIKV infection [14]. CHIKV infects a variety of cell types, including dendritic cells, macrophages, synovial fibroblasts, endothelial, and myocytes [15]. In humans, it also infects osteoblasts and induces joint pathology and erosive arthritis [16]. CHIKV infection has been shown to cause strong upregulation of the arthritis-related genes RANTES/CCL5 and IL-8 in human synovial fibroblasts using single-cell sequencing technology [17]. In macaques, long-term CHIKV RNA expression correlated with a mononuclear cell infiltration in the synovial tissue. A hallmark of CHIKV chronic infection is the persistence of activated macrophages, with viral RNA and viral antigens detectable in lymphoid tissues and the liver for up to several months, accompanied by centrilobular hepatocellular swelling, necrosis, and high levels of hepatocyte death [18]. This indicates that the liver might be a latent site of CHIKV. In IFN-I deficient mice, the virus was found to replicate explosively in the liver, and then the virus would attack muscle, joint, skin fibroblasts, etc. [19].
The liver serves as a crucial frontline immune organ in the body, capable of detecting pathogens that enter through the intestinal tract [20]. It contains the largest population of phagocytic cells in the organism. The default immune state of the liver is anti-inflammatory or immunotolerant; however, upon stimulation, it can mount a rapid and potent immune response. The balance between immunity and tolerance is crucial for liver function. Viral infections, drug- and alcohol-induced liver injury, and autoimmunity can disrupt hepatic immune homeostasis. Excessive inflammation may lead to liver damage and remodeling, while insufficient immunity may result in chronic viral infection. The dynamic equilibrium among various immune cells in the liver is key to maintaining hepatic health [21,22]. The large number of macrophages in the liver can capture pathogens from the blood and eventually kill them through various actions [23]. The adequate activation of both CD4+ and CD8+ T cells is essential for the effective clearance of pathogenic infections. CD8+ T cells serve as one of the primary effectors in antiviral immunity, while CD4+ T cells contribute indirectly to infection clearance by modulating the activity of other immune cells, including macrophages, neutrophils, B cells, and CD8+ T cells [24]. Furthermore, upon activation, NKT cells release cytotoxic granules containing molecules, such as perforin and granzymes, to eliminate target cells [25]. The loss of balance among hepatic immune cells may be a critical factor contributing to liver injury. Thus, the present study was designed to elucidate the changes of immune response in the liver and the possible causes of liver damage following CHIKV infection through single-cell sequencing.

2. Materials and Methods

2.1. Animals, Virus Strain, and Biosafety Statement

Six rhesus macaques (male, 2–4 years old) were randomly assigned to an infection group (CHIKV) (n = 3) and a control group (NC) (n = 3). All animals tested negative for CHIKV prior to the experiment. All animal procedures were approved by the Institutional Animal Care and Use Committee of the Institute of Medical Biology, Chinese Academy of Medical Sciences (Approval No.: DWSP20210006). The CHIKV strain (Asian genotype, GenBank: MH670649.1) was sourced from the National High-Level Biosafety Primate Research Center in Kunming, Yunnan. All experiments involving live virus infection and handling of infectious samples were conducted in ABSL-3 facilities at the National High-Level Biosafety Primate Research Center in Kunming, Yunnan.

2.2. Viral Challenge and Tissue Collection

Animals were individually housed under identical conditions. The infection group received an intramuscular injection of 2 × 107 PFU of CHIKV into the hind limb, while the control group was administered an equal volume of PBS. On day 7 post-infection, animals were euthanized, and fresh liver tissues were collected for the preparation of single-cell suspensions and subsequent single-cell sequencing.

2.3. Preparation of Single-Cell Suspensions

Fresh liver tissue was minced with scissors and transferred to pre-warmed (37 °C) glucose Krebs Ringer solution (Merck, Darmstadt, Germany, Cat# K4002-10X1L) containing 0.5% collagenase (Merck, Cat# C4-28-100MG). The homogenate was then ground and filtered twice through a 40-μm cell strainer. The filtrate was centrifuged at 300× g for 10 min. The cell pellet was washed twice with phosphate-buffered saline (PBS, Gibco, New York, NY, USA, Cat# A569701) containing 1% fetal bovine serum (FBS, Gibco, Cat# A10091148) and finally resuspended in the same buffer for subsequent processing.

2.4. Preliminary Processing of Single-Cell Sequencing Raw Data

Raw sequencing data underwent quality control using FASTQC (v0.11.2). The Cell Ranger software suite (www.10xgenomics.com/support/software/cell-ranger, accessed on: 28 July 2024) was employed to align reads to the rhesus macaque reference genome (Macaca mulatta, assembly version Mmul_10, Ensembl. Available at: https://ensembl.org/Macaca_mulatta/Info/Index, accessed on: 16 May 2024) and perform initial data processing, including barcode filtering, UMI counting, and gene expression matrix generation. Dimensionality reduction (PCA, UMAP/t-SNE), clustering, and cell type annotation using the Seurat R package (v5, satijalab.org/seurat) followed.

2.5. Cell Population Definition

To achieve high-precision and biologically meaningful cell type annotation, we adopted an iterative refinement strategy. Initial Annotation: First, we performed preliminary automated cell type annotation on the raw gene expression matrix using Single R (v2.4.1). The initial annotation results were then imported into the Seurat (v4.2.0) visualization environment for in-depth evaluation. We validated the accuracy of the automated annotation by examining the expression patterns of known cell type marker genes. Cell populations with ambiguous labels, low confidence, or clear contradictions to marker gene expression profiles were manually reviewed and corrected. Based on the expert evaluation results, we selected all cells with high-confidence labels (including manually corrected cells and those with highly reliable automated annotations). The aforementioned process was repeated multiple times until the cell type annotations stabilized across all relevant samples and showed complete concordance with known biological markers. Through this iterative approach, we ultimately obtained high-quality, study-specific cell annotation results, ensuring the accuracy and reliability of cell classification throughout the entire process from discovery to validation.

2.6. Screening of Differentially Expressed Genes (DEGs) and Functional Enrichment Analysis

First, the cells were grouped according to their respective samples. To minimize the interference of individual variation on sequencing results, we performed differential analysis on cells between samples within the same subpopulation using the methods of FindMarkers and pseudo-bulk. Gene expression values were represented as the average expression across samples within the same subpopulation. The criteria for screening differentially expressed genes were as follows: Upregulated: log2FC ≥ 1 and p-value ≤ 0.05 and expression proportion (pct) ≥ 0.1; Downregulated: log2FC ≤ −1 and p-value ≤ 0.05 and expression proportion (pct) ≥ 0.1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the identified DEGs. Data visualization was carried out using ggplot2 (R package) and GraphPad Prism 8.

2.7. Histopathological Section Analysis

Tissues were fixed in 4% formaldehyde solution for 72 h, embedded in paraffin, and sectioned into 5-μm thick slices for hematoxylin and eosin (H&E) staining. Sections were scanned using a 3DHISTECH scanner (3DHISTECH Ltd., Budapest, Hungary). Pathological evaluation of H&E-stained sections was performed by an experienced pathologist using the manufacturer’s CaseViewer software (v2.4).

2.8. Viral Load Detection

Blood samples were thoroughly mixed with three volumes of TRI Reagent (Invitrogen, AM9738) to inactivate the virus. For liver tissue, 100 mg samples were homogenized with 800 µL of TRI Reagent for viral inactivation. RNA extraction was performed automatically using the KingFisher Flex Purification System (ThermoFisher, New York, NY, USA) with the MagMAX-96 Nucleic Acid Kit (ThermoFisher, AM1836), following the manufacturer’s instructions. Quantitative PCR (qPCR) was conducted on a CFX96 Touch Real-Time PCR Detection System (Bio-Rad, California, CA, USA) using the TaqMan Fast Virus 1-Step Master Mix (ThermoFisher, 4444432) to quantify viral load. The primers and probe targeted the E gene of CHIKV: CHIKV-E-F: 5′-CTC ATACCGCATCCGCATCAG-3′; CHIKV-E-R: 5′-ACA TTGGCCCCACAATGAATTTG-3′; CHIKV-E-Probe: 5′FAM-TCCTTAACTGTGACGGCATGGTCGCC-BHQ1-3′. Viral load in the samples was calculated based on a standard curve generated from a plasmid containing the CHIKV E gene.

2.9. Statistical Analysis

The statistical analysis mentioned in the text was performed using T-tests in GraphPad Prism 8.

3. Results

3.1. CHIKV Infection Causes Liver Injury in Rhesus Macaques

To characterize the specifics of CHIKV infection in rhesus macaques, blood samples were collected at various time points to measure viral load and serum levels of ALT (Alanine Aminotransferase) and AST (Aspartate Aminotransferase). On day 7 post-infection (dpi), animals were euthanized for liver collection to assess viral load, perform pathological analysis, and prepare samples for single-cell sequencing. High levels of viremia were detected at 1 dpi, which declined rapidly by days 2–3 to below the limit of detection (Figure 1A). Serum AST levels were persistently elevated, while ALT levels showed a transient increase on day 2 (Figure 1B). Shifts in ALT and AST levels may indicate that liver function is affected by CHIKV infection. But progression of infection leads to concomitant increases in serum AST/ALT levels [18]. Although no viral load was detected in the liver of rhesus macaques euthanized at 7 dpi, histopathological examination revealed liver injury characterized by vascular hemorrhage or thrombosis, lymphocyte infiltration, localized hepatocellular swelling, and fatty vacuolation (Figure 1C). In fact, our current study builds upon a previously characterized rhesus macaque model infected with the same CHIKV strain, which also showed an absence of viral load in liver tissue [26]. These findings demonstrate that CHIKV infection causes liver injury in rhesus macaques. Therefore, we aim to employ liver single-cell sequencing to profile the characteristics of liver injury indirectly induced by CHIKV infection.
Figure 1. Establishment of the rhesus monkey model of CHIKV infection. (A). Change of viral load in the blood of rhesus after CHIKV infection. (B). Change of ALT/AST levels in the serum of rhesus monkeys after CHIKV infection. (C). Pathological damage staining and score of 7dpi CHIKV infection.*** p < 0.001.

3.2. Single-Cell Sequencing Landscape

To investigate the potential mechanism underlying CHIKV-induced liver injury and characterize the hepatic cellular composition, we performed single-cell RNA sequencing (scRNA-seq) on liver cells (Figure 2A,B) and confirmed inter-sample heterogeneity (Figure 2C). Single-cell sequencing detected a total of 94,036 cells (NC = 55,387; CHIKV = 38,649). Based on distinct gene expression profiles, scRNA-seq classified intrahepatic cells into 29 clusters. Using established cell-specific markers (Figure 2D and Figure S1), these clusters were annotated into 13 cell types: The most abundant were T cells and macrophages (Macs), followed by hepatocytes, endothelial cells, and B cells. Minor populations included cycling cells, erythroid cells, mesenchymal cells, plasma cells, pDCs, and an “other” category comprising cells not defined by current markers (Figure 2E). Post-infection, we observed a pronounced reduction in the proportion of B cells. In contrast, the proportion of inflammatory macrophages showed a modest increase (Figure 2F).
Figure 2. Overview of hepatic single-cell sequencing. (A). Experimental strategy for liver single-cell sequencing. (B). Workflow of single-cell sequencing and analytical pipeline. (C). Cluster annotation of NC (NC 1–3) and CHIKV (CHIKV 1–3) groups. (D). Expression of marker genes across distinct cell populations. (E). Classification of major cell types in NC and CHIKV groups form of UMAP. (F). Proportional difference in major cell types between NC and CHIKV groups.

3.3. Abnormal Changes in Hepatocytes and Liver Endothelial Cells Following Liver Injury

To clarify the functional status of hepatocytes and endothelial cells after liver injury, we first subsetted hepatocytes using markers such as HP and ALB (Figure 3A) and liver endothelial cells using markers including CDH5 and SPARCL1 (Figure 3E). Functional enrichment analysis of these subsets revealed multiple dysfunctions in hepatocytes post-injury. For example, upregulation of signaling pathways related to viral infection and heightened antigen presentation signals were observed, suggesting enhanced ability of hepatocytes to present antigens to immune cells. Concurrently, numerous immune pathways involving T cells and lymphocytes were activated. Additionally, downregulation of coagulation cascade-related genes (e.g., FGG, FGB) and metabolic genes (e.g., CYTB, COX1, GSTB1) indicated overall metabolic impairment in hepatocytes, leading to disrupted metabolism of fats, carbohydrates, and drugs (Figure 3B–D). These dysfunctions may further hinder liver repair. In endothelial cells, alterations in antigen presentation signals were detected, accompanied by elevated TNF signaling, T cell activation, and primary immune responses. Similar downregulation of metabolic genes was also observed (Figure 3F–H). These findings suggest that during CHIKV infection, the liver assumes an antigen-presenting role, stimulating T cell stress and activation. Furthermore, CHIKV infection likely causes injury to both hepatocytes and endothelial cells.
Figure 3. Functional analysis of differentially expressed genes (DEGs) in hepatocytes and liver endothelial cells after liver injury. (A). Marker genes defining hepatocytes. (B). Volcano plot of DEGs in hepatocytes. (C,D). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs in hepatocytes. (E). Marker genes defining liver endothelial cells. (F). Volcano plot of DEGs in liver endothelial cells. (G,H). GO and KEGG enrichment analysis of DEGs in liver endothelial cells.

3.4. Immune Characteristics of B Cells and Plasma Cells

To investigate the specific causes and mechanisms underlying B cell reduction, we first subsetted B cells using markers such as CD79A and MS4A1 (Figure 4A) and plasma cells using markers including MZB1 and JCHAIN (Figure 4D). Subsequent functional analysis of differentially expressed genes (DEGs) in the B cell population revealed alterations in multiple signaling pathways in CHIKV-infected hepatic B cells, including phagosome formation, antigen presentation, viral infection-related signaling, and naïve immune response. Conversely, genes associated with mitochondrial energy metabolism (e.g., COX3) and cellular survival stability (e.g., CAMP) were downregulated (Figure 4B,C). In plasma cells (effector B cells), changes were observed in pathways related to ribosomal translation, antigen presentation, and immune response (Figure 4D–F). These alterations may reflect a mechanism by which plasma cells enhance protein translation levels to increase antibody and immunoglobulin production following infection.
Figure 4. Immune characteristics of B cells and plasma cells. (A). Marker genes defining B cells. (B). Volcano plot of DEGs in B cells. (C). KEGG enrichment analysis of DEGs in B cells. (D). Marker genes defining plasma cells. (E,F). GO and KEGG enrichment analysis of DEGs in plasma cells.

3.5. Subclassification of T Cells and Macrophages

To characterize the specific status of hepatic immune cells and their subsets following viral infection, we performed further subclustering based on cell-specific gene expression profiles. T cells were subdivided into CD8+ T, CD4+ T, NKT, and memory T cell subsets. While the proportions of NKT, CD4+ T, and CD8+ T cells exhibited some variation, these changes were not statistically significant. Inflammatory and non-inflammatory macrophages were further classified into conventional macrophages (Macs), TCR+ macrophages, and tissue resident macrophages (TAMs). Among inflammatory macrophages, CHIKV infection led to an increased proportion of conventional Macs and a reduction in TCR+ macrophages (Figure 5C,D). In contrast, within the non-inflammatory macrophage compartment, the proportions of tissue resident Macs and TCR+ macrophages were elevated (Figure 5E,F). These findings indicate that CHIKV infection alters the relative abundances of T cell and macrophage subsets, potentially disrupting hepatic immune homeostasis. Functional imbalance among these immune cell populations may contribute to virus-induced liver injury.
Figure 5. Subtype classification and proportional analysis of immune cell subsets. (A,C,E). Subclustering of T cell, inflammatory macrophage, and non-inflammatory macrophage subpopulations. (B,D,F). Top 10 expressed genes and proportional differences among subpopulations of T cells, inflammatory macrophages, and non-inflammatory macrophages.

3.6. T Cell Immune Activation

We performed detailed functional enrichment analysis on each T cell subset. The results revealed that CD8+ T cells exhibited an activated immune response, with significant alterations in pathways including NK cell-mediated cytokine signaling, T cell receptor signaling, TNF signaling, and inflammatory cytokine-related pathways. Key cytotoxic genes such as GZMB and GZMA were markedly upregulated (Figure 6A–C). In NKT cells, immune activation-related pathways were substantially altered, including T cell receptor signaling, cytokine signaling, cytotoxicity, and macrophage activation pathways. Activation markers including CD69 and KLRG1 were significantly upregulated (Figure 6D–F). CD4+ T cells primarily exerted immunoregulatory functions, with changes in critical pathways such as T cell receptor signaling, NK cell-mediated cytokine signaling, antigen presentation and processing, naïve immune signaling, and complement and coagulation cascades (Figure S2A–C). Memory T cells showed alterations primarily in immune activation-related pathways, including NK cell activation, lymphocyte activation, and cytotoxicity (Figure S2D–F). Collectively, these findings indicate that all T cell subsets were in a state of immune activation. CD8+ T cells and NKT cells likely promoted cytotoxicity through increased expression of cytotoxic factors. Although T cell activation is beneficial for antiviral defense in the liver, it may also contribute to liver injury. In particular, altered cytotoxicity mediated by CD8+ T and NKT cells may lead to non-specific killing of hepatocytes. Furthermore, activated T cells can further enhance the immune status of macrophages [27]. Immunofluorescence staining revealed that in the liver after CHIKV infection T cells appeared to be in closer proximity to hepatocytes. Additionally, there were more dead cells observed in the liver, and it seemed that some T cells had aggregated around the apoptotic cells. (Figure S3). However, due to the lack of available rhesus monkey-specific antibodies, the use of humanized antibodies may compromise the authenticity of the experimental results.
Figure 6. T cell immune activation. (A). Volcano plot of DEGs in CD8+ T cells. (B,C). GO and KEGG enrichment analysis of DEGs in CD8+ T cells. (D). Volcano plot of DEGs in NKT cells. (E,F). GO and KEGG enrichment analysis of DEGs in NKT cells.

3.7. Activation of Macrophages

As an indispensable component of the immune cell repertoire, macrophages play a critical role in the immune system. Therefore, we further analyzed the functional states of macrophage subpopulations. In conventional inflammatory macrophages, pathways mediating phagocytic function—including Fc gamma R-mediated phagocytosis, Toll-like receptor, and the NF-Kappa B signaling pathway—were altered. Additionally, changes were observed in the antigen presentation signals, and the NOD-like receptor signaling pathway (Figure 7B,C). Activation-associated genes such as TLR2, TLR4, and CXCL8 were significantly upregulated (Figure 7A). In inflammatory tissue resident macrophages, alterations primarily involved immune response signaling, leukocyte activation, lymphocyte activation, T cell activation, and cytotoxicity (Figure 7D,E). Inflammatory TCR+ macrophages exhibited changes in T cell receptor signaling, antigen presentation signals, and the NF-Kappa B signaling pathway (Figure 7F).
Figure 7. Activation of inflammatory macrophages. (A). Differential expression of macrophage activation-related genes among subsets of inflammatory macrophages. (B,C). GO and KEGG enrichment analysis of DEGs in conventional inflammatory macrophages. (D,E). GO and KEGG enrichment analysis of DEGs in tissue resident inflammatory macrophages. (F). KEGG enrichment analysis of DEGs in TCR+ inflammatory macrophages.
In conventional non-inflammatory macrophages, alterations were noted in pathways such as autophagy, bacterial invasion of epithelial cells, and ferroptosis. Non-inflammatory tissue resident macrophages showed changes in immune signaling, antigen presentation processes, leukocyte adhesion and activation, as well as lysosomal function, ferroptosis, and apoptosis (Figure S4). In summary, these results demonstrate that macrophages are activated following CHIKV infection, particularly inflammatory subsets. These activated macrophages may not only further modulate T cell activity but also clear pathogens and compromised cells through mechanisms such as Fc gamma R-mediated phagocytosis and NETosis. However, this enhanced phagocytic activity may also impact healthy cells.

4. Discussion

Numerous mouse models are available for investigating various aspects of CHIKV pathogenesis and immunity [28]; however, given the immunological and physiological similarities, elucidating the details of CHIKV infection in non-human primates (NHPs) may be more relevant to human infection and disease. Consequently, NHP models of CHIKV infection can more accurately predict the effects of antiviral therapies and vaccines in humans [29]. In this study, we observed that early CHIKV infection induces liver injury in rhesus macaques, evidenced by histopathological damage in the liver, consistent with clinical findings [30,31]. Single-cell omics sequencing further revealed immune activation in the liver, suggesting that coordinated interactions among various immune cell populations may contribute to liver injury, unlike previous reports [18,19]. Although no viral genome was detected in the liver or blood at 7 dpi, it remains unclear whether hepatic infection occurred earlier and was subsequently cleared by the immune system or whether liver infection manifests at a later stage. Our prior studies characterized CHIKV infection in rhesus macaques and C57BL/6J mice. Potentially due to differences in viral strains, no viral genome was detected in the livers of either mice or macaques, yet pathological liver damage was observed in both [26]. This suggests that early hepatic injury observed in our study may stem from systemic inflammation and indirect activation of immune cells rather than direct viral replication. While CHIKV-induced liver damage in other NHP is often accompanied by actual hepatic infection, pregnant rhesus macaques with CHIKV infection also exhibit hepatic pathological damage—including lymphocyte infiltration—despite undetectable viral genome [32]. Similarly, severe liver pathology has been observed in infected cynomolgus macaques, where elevated AST and ALT levels during the mid to late stages of infection may be associated with hepatocyte apoptosis [18]. Furthermore, aged rhesus macaques show diminished immune responses and reduced viral clearance capacity upon CHIKV infection [33]. Collectively, these observations suggest a dual mechanism for CHIKV-induced hepatopathology: direct viral cytopathy and indirect activation of immune cells induce inflammation-mediated damage. Downregulation of genes associated with coagulation, oxidative respiration, and metabolism further supports the presence of liver damage, though whether these changes are a cause or consequence of injury remains uncertain [34]. The reduction in B cell proportion in the liver may be attributed to multiple factors: migration of B cells to other infected tissues [35,36], inflammatory cytokine-mediated B cell damage resulting from immune cell activation [37], and direct CHIKV-induced B cell death [38]. CHIKV-induced hepatocellular injury may partly stem from virus indirectly triggered activation of hepatic immune cells, leading to immune imbalance. Viral infection indirectly alters cytokine levels in T cells and macrophages, prompting the release of cytotoxic factors from multiple immune cell types. These intermediates may then exert cytotoxic effects on hepatocytes.
The observed pan-immune signature in our sequencing results reflects a comprehensive and robust immune activation across the liver following CHIKV infection. This indicates that the immune cell types participating in liver immunoregulation are highly diverse, encompassing both the adaptive and innate immune systems, which together form a complex immune cell ecosystem. Notably, defined cell subsets, including CD8+ T cells and NKT cells, exhibited robust cytotoxic activity through the overexpression of genes such as GZMB and GZMK, highlighting the multifaceted immune activation in the infected liver. In addition, the activation of inflammatory cytokine signaling in T cells and macrophages implies that hepatic immune activation likely features a dual profile of cytotoxicity and inflammatory mediator aggregation [39].
Under specific conditions, both hepatocytes and endothelial cells can function as antigen-presenting cells, activating CD4+ and CD8+ T cells against blood- or gut-derived antigens [40,41]. Stimulated CD8+ T cells can migrate within liver sinusoids and recognize infected hepatocytes or endothelial cells [42]. When pathogens enter the liver, they rapidly trigger immune responses, leading to inflammation, pathogen clearance, and ultimately tissue repair. However, chronic inflammation may result in extensive immune cell recruitment and tissue damage. Persistent inflammatory responses can further drive liver fibrosis and functional impairment. CD4+ and CD8+ T cells play central roles in controlling and eliminating hepatic viral infections. We observed that, following CHIKV infection, CD8+ T cells in the liver excessively secreted GZMB and KLRD1. In hepatitis C virus (HCV) infection, robust populations of HCV-specific CD4+ T cells can drive CD8+ T cell-mediated cytotoxic T lymphocyte (CTL) responses [43]. Furthermore, hepatic expansion of CD8+ T cell and macrophage populations during extrahepatic viral infection can also contribute to liver injury [44]. SARS-CoV-2 infection can lead to liver injury through multiple pathways, including cytokine storm [45]. NKT cells utilize inherent cytotoxic mechanisms to target cells for clearance while promoting cellular activation and cytokine production [25]. Sequencing data indicated that NKT cells were highly activated, with overexpression of molecules such as GZMK, KLRG1, and CD69. Signals enhancing NKT cell cytotoxicity have been found to be enriched across multiple types of immune cells. Previous research has shown that cytokines from other immune populations can activate NKT cells to exert effector functions similar to conventional cytotoxic T cells [46]. Although interferon-related signaling is typically activated during viral infections, our data did not show significant interferon responses. In reality, strong interferon signaling is induced during the early stages of viral infection but often diminishes rapidly within 3–7 days [47,48].
Macrophages combat viral infections by modulating host physiological and pathological responses. Their phagocytic activity is essential for host defense against infections [49]. Following viral infection, macrophages enhance the expression of inflammatory cytokines, thereby activating antiviral immunity [50]. We observed in inflammatory macrophages, after CHIKV infection, excessive expression of TLR4, TLR2, CXCL8, S100A8/9, and TNF family genes. This may further amplify antiviral immunity in the liver. However, persistent macrophage infiltration and sustained secretion of inflammatory factors following CHIKV infection can also cause damage to healthy tissues [51]. Hepatic macrophages exhibit diverse functions, simultaneously promoting both hepatocyte apoptosis and proliferation, depending on the overall immune status of the liver [52,53,54]. In the early stages of injury, infiltration of inflammatory macrophages into the damaged liver can exacerbate organ damage [55]. During the early phase of viral infection, liver-resident macrophages produce abundant pro-inflammatory cytokines, demonstrating antiviral activity. In chronic infections, however, hepatic macrophages may suppress antiviral defense by producing factors such as IL-10, TGFβ, PD-L1, and PD-L2 [56]. Furthermore, in hepatitis C virus infection, increased expression of CD163, CD33, CD80, and CD40 has been observed in liver-resident macrophages [57]. These findings underscore the importance of macrophage-mediated immune balance in maintaining normal liver physiology.
We observed that there may be some potential bidirectional activation effects between hepatic T cells and macrophages after CHIKV infection [27], but, due to the lack of a comprehensive cell–cell communication database for rhesus macaques, we were unable to elucidate the specific mechanistic details of their interactions. Furthermore, due to a lack of experimental materials, the specific details and validity of this bidirectional activation were not confirmed through in vitro co-culture assays. Overall, aberrant activation of CD8+ T cells, macrophages, and NKT cells may contribute to liver injury. In fact, multiple immune cell types—including NKT cells and even nonspecific CD8+ T cells—can mediate the destruction of healthy hepatocytes during viral infection [58]. H1N1-induced liver pathology, despite the virus’s inability to directly infect hepatocytes, is primarily mediated through immune cell activation and inflammatory cytokine release [59,60]. Therefore, in combating CHIKV infection, it is crucial to appropriately maintain immune balance among various cell populations to avoid secondary tissue damage.
Although this study is the first to characterize the hepatic immune landscape underlying indirect liver injury following CHIKV infection in rhesus monkeys through single-cell sequencing, several limitations should be noted. The scarcity of experimental rhesus monkeys and the high associated costs restricted the sample size to only three animals, which precludes robust analysis of long-term immune dynamics or sex-based differences post-infection. Moreover, the lack of established protocols and reference data for rhesus monkey single-cell sequencing may introduce methodological variability. Additionally, validation of sequencing findings was hindered by the unavailability of rhesus monkey-specific antibodies; we attempted to use human- and mouse-derived antibodies but did not obtain reliable data. Further research is necessary to address these limitations and enable a deeper exploration of long-term liver pathology associated with CHIKV infection.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/v18020201/s1, Figure S1: Definition of Marker Genes for Different Cell Clusters in the Liver; Figure S2: Immunological profiling of CD4⁺ T cells and memory T cells; Figure S3: Immunofluorescence analysis of T cell localization with hepatocytes and apoptotic cells in the liver (Scale bar 20 μm); Figure S4: Immunological characteristics of non-inflammatory macrophages.

Author Contributions

S.L. design the study. S.L. and J.W. providing the essential resources. H.Y. and Y.Y. wrote the manuscript. Y.Y., Q.H., H.Y., Y.Z., C.T., J.W., W.Y., H.W., D.W. and W.Q. performed experiments. H.Y. and Y.Y. analyzed data. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-038, 2023-I2M-2-001); Major project of basic research in Yunnan Province (202401BC070008); High-Level Medical Talent Training Program of Yunnan Province (L-2024018).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Animal Care and Use Committee of the Institute of Medical Biology, Chinese Academy of Medical Sciences (Approval No: DWSP20210006).

Data Availability Statement

The data included in this study are available upon request from the corresponding author.

Acknowledgments

We would like to thank all the staff at the National Kunming High-level Biosafety Primate Research Center for their assistance and support in this study.

Conflicts of Interest

The authors declare no conflict of interest.

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