Uncovering the Single-cell Transcriptomic Signatures and Pathogenesis of Mucosal-associated Invariant T cells during Nonalcoholic Steatohepatitis

doi:10.21203/rs.3.rs-3964596/v1

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Uncovering the Single-cell Transcriptomic Signatures and Pathogenesis of Mucosal-associated Invariant T cells during Nonalcoholic Steatohepatitis

https://doi.org/10.21203/rs.3.rs-3964596/v1

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Background

As an inflammatory subtype of nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH) has turned into a major trigger of liver cirrhosis and liver-associated deaths worldwide. Longitudinal studies have indicated the T lymphocyte-associated immunodysfunction in the pathogenesis of NAFLD, yet the detailed information of the subsets including Mucosal-associated Invariant T (MAIT) cells in NASH is largely obscure.

Methods

In this study, we isolated peripheral blood-derived mononuclear cells (PBMCs) from NASH patients and healthy controls (HC), and dissected the single-cell transcriptomic signatures of immune cell sub-clusters and MAIT cells by conducting multifaceted bioinformatics analyses. Meanwhile, the distribution and expression of MAIT cells and the candidate biomarkers (e.g., GADD45B, STAT1, CCL4, RPL38) in liver tissues or PBMCs was identified by immunostaining (e.g., IHC, IF), qRT-PCR and western-blotting analysis. Additionally, the STAT1-mediated network in MAIT cell-related regulatory mechanism of NASH was explored as well.

Results

Compared to the HC group, NASH patients revealed multifaceted variations in the distribution of MAIT cells and the relative immune cells in PBMCs. In detail, MAIT cells were collectively accumulated in PBMCs and liver tissues of NASH patients, which revealed a distinct distribution pattern from the HC group according to the 7 sub-clusters. Of the indicated candidate biomarkers for clinical diagnosis, STAT1-T-bet axis served as the pathogenic mechanism of NASH via mediating MAIT cell differentiation and inflammatory response.

Conclusion

Overall, our data illuminated the single-cell transcriptomic signatures of MAIT cells and the concomitant sub-clusters in NASH patients. Our findings put forward the involvement of MAIT cells in NASH, which would benefit the further dissection of the MAIT cell-related pathogenesis and clinical diagnosis of NASH.

Nonalcoholic steatohepatitis (NASH)

Mucosal-associated invariant T cells (MAITs)

Single-cell RNA-SEQ

Immunodysfunction

STAT1

Nonalcoholic steatohepatitis (NASH) is characterized by accumulation of fat and chronic inflammation in liver tissue, which has been recognized as the most severe subtype of non-alcoholic fatty liver disease (NAFLD) and can further evolve into liver cirrhosis or hepatocellular carcinoma (HCC)[1, 2]. Generally, the incidence of NAFLD is ranging from 20–25% worldwide, while 25% and 5% of them further deteriorate into liver cirrhosis and HCC, respectively[3, 4]. As an inflammatory and progressive subtype of NAFLD, NASH is related with disease progression and development of cirrhosis but under-recognized in clinical practice[3, 5]. Despite the limited progress in noninvasive assessment, liver biopsy have been respectively considered as the mainstay of diagnosis[2, 3]. Worse still, no NASH-specific treatment including pharmacotherapy has been developed, and lifestyle modification (e.g., exercise, weight loss) and liver transplantation are the current mainstay of treatment[5, 6]. State-of-the-art literatures have indicated that immune cells in liver show diversity and further evolve during disease progression, which directly affect the severity of NASH[7]. Therewith, there’s an urgent need to further uncover the pathogenesis of immune cells during NASH, which will benefit the development of novel diagnosis and targeted therapeutic regimens[6–9].

Mucosal-associated invariant T cells (MAIT cells) are evolutionarily conserved unconventional T cells with αβ T-cell receptor (TCR) expression, which are recognized as a pivotal bridge between adaptive and innate immunity for the defense against diverse pathogens in mucosal tissues and blood[10, 11]. Meanwhile, MAIT cells are involved in numerous autoimmune and immune-relevant diseases (e.g., rheumatology, cancer) as well as the maintenance of liver or microbial homeostasis and intestinal barrier integrity via the production of cytokines and cytotoxic molecules[11–13]. In recent years, the physiological defensive role of MAIT cells in maintaining the immune homeostasis of liver and the concomitant pathogenic effect during liver inflammation and chronic liver diseases have been newly identified[14, 15]. For instance, MAIT cells are declined in the blood and brain of patients with liver fibrosis or end-stage cirrhosis, whereas manifest hyperactivation in the fibrotic interval and accelerate profibrogenic hepatic stellate cell (HSC) progression via promoting local inflammatory response[16, 17]. Of note, Aizarani et al reported the cellular composition and heterogeneity of liver cells between healthy donors and hepatocellular carcinoma by performing single-cell RNA sequencing, while Toubal et al and Sawada et al respectively demonstrated the pro-inflammatory effect and intestinal dysbiosis of MAIT cells during metabolic dysfunction-asssociated obesity and steatotic liver disease[18, 19]. However, the role of MAIT cells in NASH is still largely obscure, and in particular, the dysimmunity and profibrotic effect of specific subpopulations of MAIT cells in circulating blood and in situ liver tissues.

For the purpose, we took advantage of single-cell RNA-sequencing (scRNA-SEQ) and multifaceted bioinformatics analyses to dissect the variations in the content of immune cell subsets in peripheral blood of healthy donors and NASH patients. With the aid of pathological analysis of liver tissue, we verified the pathogenicity of MAIT cells during NASH, which would collectively benefit the further development of diagnosis and treatment of NASH in clinical practice.

Patient enrollment and specimen collection

4 Patients with NASH (NASH) and 4 healthy controls (HC) were enrolled (gender and age matching) in The First Affiliated Hospital of Kunming Medical University from October 2022 to May 2023. Peripheral blood and liver tissues were collected with the informed consent of the participants and the approval of the Ethics Committee of the First Affiliated Hospital of Kunming Medical University (approval number: 2020-L-08).

Inclusion criteria of NASH (CAP > 238dB/m, LSM > 6.6kPa) and HC (CAP < 238dB/m, LSM < 6.6kPa) were formulated according to the controlled attenuation parameter (CAP) value and the liver stiffness measurement (LSM) value quantified by FibroScan (502 model, Echosens, France) as we described before[20, 21]. In detail, no hepatic steatosis, CAP < 238 dB/m; mild hepatic steatosis: 238 dB/m < CAP < 258 dB/m; moderate hepatic steatosis: 259 dB/m < CAP < 291 dB/m; severe hepatic steatosis: CAP > 292 dB/m. No liver fibrosis: F0 < 6.6 kPa; mild liver fibrosis, F1 = 6.6-8.0 kPa; significant liver fibrosis: F2 = 8–11 kPa; advanced liver fibrosis: F3 ≥ 11.0kPa; liver cirrhosis: F4 ≥ 15.0kPa.

Exclusion criteria are as following: (1) a history of alcohol consumption (over 140g/week for men, and over 70g/week for women); (2) Specific diseases associated with fatty liver disease, including viral hepatitis, drug-induced liver disease, total parenteral nutrition, hepatolenticular degeneration, and autoimmune liver diseases; (3) age < 45-year-old, pacemaker installation, open wound in right upper abdomen and ascites; (4) patients with malignant tumors or severe diseases in liver, kidney, heart, and brain. Supplementary Table S1. The detailed information of HC and NASH patients.

Preparation of Peripheral blood mononuclear cells (PBMCs)

A total volume of 5 ml fresh peripheral blood were aseptically collected into EDTA tube (WEGO Group Co., LTD, China) and diluted with an equal volume of phosphate buffer solution (PBS) (Tianjin Hao Yang Biological Manufacture Co., Ltd, China). Then, PBMCs were isolated by utilizing the Ficoll (GE Ficoll Paque Premium, SCILOGEX, USA)-based density gradient centrifugation (700×g for 20 min) as we recently reported with several modifications[22, 23]. After that, the PBMCs were washed with 1×PBS (Solarbio, China) and cryopreserved in liquid nitrogen for further analyses.

Western-blotting analysis

Cells were collected and lysed with RIPA buffer containing protease inhibitors, and the total protein concentration was determined by BCA protein concentration assay kit (ThermoFisher, USA) as we described before[24, 25]. For quantitative analysis of the indicated proteins, the samples were loaded to 10% SDS-PAGE gel and transferred to PVDF membranes (Bio-Rad, USA). After blocking with 5% milk in 1×PBS for 2 hr, the membranes were incubated with the primary antibody at 4 ℃ for 12 hr and the corresponding HRP-conjugated secondary antibody (GE Healthcare) at 37 ℃ for 2 hr, respectively. The membranes were rinsed with 1×PBST buffer (1×PBS containing 0.1% Tween 20) for 3 times, and the ECL Detection Reagent (Sigma-Aldrich, USA) was used for development. Finally, the proteins in the membranes were detected by the Super-Signal West Pico Chemiluminescent Substrate (Pierce), and the gray value of the target proteins was analyzed by Image J software (NIH, USA). β-Actin was used as an internal reference. The information of the antibodies was available in Supplementary Table S2.

Total RNA extraction and quantitative real-time PCR (qRT-PCR) analysis

Total RNAs were extracted by utilizing the Trizol reagent (ThermoFisher, USA) according to the manufacturer’s instructions as we reported before[26, 27]. Then, reverse transcription-polymerase chain reaction (RT-PCR) was performed for cDNA synthesis with the reverse transcription kit (Promaga). The expression of the indicated genes was detected by qRT-PCR assay with the SYBR Green Master Mix reaction system (Qiagen) and the ABI PRISM 7900 (Applied Biosystems). The relative expression level of the mRNA was quantified by the 2^−ΔΔCt method. GAPDH was used as an internal reference. The primer sequences for qRT-PCR analysis were available in Supplementary Table S3.

Flow cytometry (FCM) assay

FCM assay was performed as we reported before[27, 28]. In detail, the PBMCs were resuspended in 1% bovine serum albumin (BSA) buffer (Life Technologies, USA) with 7-AAD addition (Bio Legend, USA). The percentage of living cells were detected by BD FACSAria I (BD Biosciences, USA) and analyzed by BD FACSDiva 8.0.1 software (BD Biosciences, USA). The information of the antibodies in FCM assay was available in Supplementary Table S4.

Single-cell RNA-sequencing (scRNA-SEQ)

ScRNA-seq libraries were generated from PBMCs of HCs and NASH patients using Chromium 5’ v3.1 (10× Genomics, Pleasanton, CA, USA). Libraries were sequenced using a Novaseq 6000 (Illumina, San Diego, CA, USA) instrument. The raw sequencing data were aligned to the human reference genome (GRCh38) and processed using the Cell Ranger software (version 4.0.0). The gene expression matrix obtained from the Cell Ranger pipeline was filtered and normalized using the Seurat R package (version 3.2). All data analysis was conducted in R (version 4.1.0). Cluster analysis was performed using the R package Seurat (version 4.0.5).

For scRNA-SEQ analysis, PBMCs were turned to 10× Chromium system (10× Genomics) and LC Sciences for library preparation according to the recommended process and the manufactures' instructions. After that, scRNA-SEQ was accomplished with HiSeq4000 system (Illumina, USA), and the quality control was realized by utilizing the 10× Cell Ranger software v1.2.0 (10×Genomics, USA). Then, the cell samples were assessed, and a total number of 68466 PBMCs (6,441 − 10,505 cells per sample, gene median = 1,221-1,789) were enriched for further bioinformatics analyses. The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA006681) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa-human.

Bioinformatics analyses of scRNA-SEQ data

For bioinformatics analyses, the gene expression matrix generated by 10× Cell Ranger software v1.2.0 (10× Genomics, USA) were turned to the R software (Seurat, version 3.0) for further data filtering. Then, the data is normalized by using a scale factor (10,000 by default) for the conversion, and the Seurat embedding function was used for the logarithmic conversion and data normalization. With the aid of RunPCA function in Seurat software, the correlation and clustering relationship of the indicated data sets were comprehensively analyzed, and subsequently visualized by the t-distributed stochastic neighbor embedding (t-SNE) of principal component. Finally, the average gene expression matrix for each cluster is retrieved, and the differentially expressed genes (DEGs) between clusters were performed using the FindAllMarkers function (only. Pos = FALSE, min.pct = 0.2, thresh. Use = 0.2) to determine the candidate biomarkers with specifically high expression in the indicated single cell subsets. The differentially expressed genes between HC and NASH was available in Supplementary Table S5.

Pseudo-time trajectory analysis

Pseudo-time trajectory analysis was conducted with the default setting of the monocle version 2.4.0 in R software. In detail, the pseudo-time sorting was performed by utilizing the reduce dimension function (parameter setting: max_components = 2, reduction_method = DDRTree), and the top 50 significant DEGs were clustered by the differential Gene Test (full Model Formula Str = Pseudotime) function and the plot_pseudotime_heatmap function in R software. Meanwhile, the Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO) analyses were performed with the online OmicStudio platform (https://www.omicstudio.cn/tool) (LC-bio, China).

Hematoxylin and eosin (H&E) staining

H&E staining was conducted as we recently described with several modifications[29, 30]. Briefly, the liver tissues were fresh-frozen in -80 ℃ in isopentane (ThermoFisher, USA) and turned to paraffin embedding for slicing. Then, the sections were placed in slides (Thermo SuperFrostPlus, USA) for H&E staining and observed under a light microscope (Olympus, Tokyo, Japan).

Statistical analysis

All statistical analysis were performed with the GraphPad Prism 6 software (GraphPad Software, San Diego, USA) as we reported[27, 28]. The data of two unpaired groups were compared with unpaired t test and the data of multiple unpaired groups were compared with the one-way ANOVA method with Tukey’s post-hoc test as we described[31]. Only when P < 0.05 was considered statistically significant. *P < 0.05; **P < 0.01, ***P < 0.001; NS, not significant.

The landscape of immune cell subtypes and decline in T cells in NASH

To decoding the omics feature of immune cell subtypes in NASH, we incipiently took advantage of scRNA-SEQ to analyze PBMCs from 4 NASH patients (NASH) and 4 healthy controls (HD) (Fig. 1A). After data filtering with R package Seurat14, a total number of 68466 single cells were automatically plotted into 25 sub-clusters and four major immune cell populations, including T cells, NK cells, B cells and monocytes (Fig. 1B-1C). Compared to those in the HC group, we found the percentage of T cells was declined whereas the T cell_NK subset was increased in NASH according to the statistical analysis of tSNE plots of PBMCs (Fig. 1D). According to UMAP diagrams, we intuitively noticed the sharp decrease in T cells and moderate decline in B cells, whereas the moderate increase in NK cells and T cell_NK (Fig. 1E-1F).

Furthermore, with the aid of specific biomarkers, the distribution of the indicated immune cell subsets was cataloged in PBMCs between the HD and NASH groups (Fig. 1G). To further verify the variations in the contents of the indicated immune cells, we turned to FCM analysis of the PBMCs and found that the percentage of total CD3⁺ T cells were moderately increased in NASH patients, whereas those of and the CD3⁺CD8⁺ T and CD3⁺CD56⁺ NKT cell subsets rather than CD3⁻CD56⁺ NK cells and the CD3⁺CD4⁺ T cell subset were decreased (Fig. 1H-1I). Taken together, NASH patients revealed diverse variations in immune signatures, and in particular, the decline in T cell subset compared to healthy controls.

The T cell spectrum and sharp increase in MAIT cells in NASH

Having verified the decline in total T cells in patients with NASH, we next plotted the 43101 retained cells in a tSNE containing the major T cell sub-clusters (Fig. 2A). Notably, the diverse variations in the contents of T cell subsets were observed between the HD and NASH groups, including MAIT cells, the CD4⁺ and CD8⁺ T cells (Fig. 2B-2C). Distinguish from CD4⁺ T cells with minimal differences, MAIT cells revealed sharp increase in the NASH group whereas the CD8⁺ T cell subsets (naïve CD8⁺ T cells, GZMK and GNLY effector CD8⁺ T cells) showed contradictory tendency (Fig. 2D-2E). As to MAIT cells, we noticed the specific distribution of MAIT cells according to tSNE plot of MAIT cell-associated biomarkers (SLC4A10, TRAV1-2) in total T cells (Fig. 2F).

In consistent with the UMAP diagram, the proportion of MAIT cells in total T cells was significantly increased in the NASH group (Fig. 2G). In detail, the content of CD161⁺TCRVα7.2⁺ MAIT cells in total T cells of PBMCs was elevated in patients with NASH when compared to the HC group (Fig. 2H). Furthermore, with the aid of IHC staining, we further confirmed the accumulation of CD3⁺MDR-1⁺ MAIT cells in portal tract and lobular area of liver tissue in NASH patients (Fig. 2I). Collectively, our data indicated the variations in the distribution pattern of T cells and the significant increase in MAIT cells in patients with NASH.

The alterations in the differentiation trajectory of MAIT cells in NASH

Subsequently, we turned to pseudotemporal trajectories to verify the transcriptional dynamics and differentiation process of MAIT cells in PBMCs of the HD and NASH groups. As shown by the pseudotime single-cell trajectory, there were five branching points in the differentiation route of immune cells, and both the HD and NASH groups revealed a similar common pattern (Fig. 3A-3C). In detail, the naïve T cells and MAIT cells were respectively located towards the left and right terminus of the bifurcations of the trajectory, whereas the CD4⁺ and CD8⁺ effector T cells were distributed throughout the trajectory (Fig. 3C). Meanwhile, we further assessed the pseudotime dynamics of differentially expressed genes (DEGs) among these subclusters and installed them into three modules according to their pseudotemporal expression patterns (Fig. 3D-3E). For instance, naïve T cells revealed minimal expression of CD160 and XCL2, whereas with high level of PYHIN1, RPL22, TGFBR3, TPM3 expression (Fig. 3D). Differ from CD4⁺ effector T cells with high-level expression of all the aforementioned genes, CD8⁺ effector T cells with minimal expression of CD160 and XCL2 (Fig. 3D-3E).

As to MAIT cells, we intuitively observed the variations in pseudotime single-cell trajectory and the distribution between the HC and NASH groups, which was further confirmed by the pseudotime dynamics of the representative DEGs among the seven sub-clusters (Fig. 3F-3I). Furthermore, the representative genes (e.g., MALAT1, MT-RNR1) as well as the seven major gene sets in total MAIT cells were intuitively observed between the HC and NASH groups (Fig. 3I-3J). For instance, as to single cells with MALAT1 expression, the HC group revealed accumulation of sub-cluster 1 and 4, whereas the NASH group exhibited enrichment of single cells in sub-cluster 0, 2, 3, and 5 (Fig. 3J). Furthermore, we intuitively noticed the distribution of indicated gene sets in the seven sub-clusters in MAIT cells, and the sing-cells with the indicated expression pattern of representative genes (e.g., SNORD3B-1 in sub-cluster 4, LEF1 in sub-cluster 6, and CCL4L2 in sub-cluster 0) was also shown according to the violin plots (Fig. 3K). Overall, these data suggested the diverse variations in the differentiation route of immune cells and MAIT cells in PBMCs between HD and NASH.

Characterization of the transcriptomic signature of MAIT cells in NASH

To further dissect the transcriptomic signature of MAIT cells in total T cells of PBMCs between the HD and NASH groups, we conducted volcano plot analysis of the upregulated (e.g., RPL38, RPS4Y1, STAT1) and downregulated (e.g., IER2, NFKB1A, MT-ND4L) DEGs, together with the relative non-DEGs based on the gene expression profiling (Fig. 4A). From the HeatMap of Pearson correlation analysis, we noticed the variations in the affinity of the indicated 7 sub-clusters between the HC and NASH groups (Fig. 4B). Furthermore, the representative DEGs (e.g., IER2, ID2, NFKBIA, STAT1, CCL4) in MAIT cells between the indicated two groups were observed as well (Fig. 4C).

With the aid of gene ontology (GO) analysis, we found the upregulated DEGs in MAIT cells between the HC and NASH groups were mainly involved in diverse immunoregulation-associated bioprocesses (e.g., protein binding, membrane, immune response, and interferon-gamma-mediated signaling pathway), whereas the downregulated DEGs were involved in protein binding and cytoplasm instead (Fig. 4D-4E). By conducing KEGG analysis, we observed that both the upregulated and downregulated DEGs were mainly involved in immune response- and metabolism-related signaling pathways such as antigen processing and presentation, chemokine signaling pathway, metabolic pathways, PI3K-Akt signaling pathway and MAPK signaling pathway (Fig. 4F-4G). Consistently, a variety of pro-inflammatory response-related gene sets (e.g., TNF-α, rosiglitazone IFN-γ/TNF/IL-4 macrophage) were enriched between the HC and NASH groups according to GESA diagrams (Fig. 4H). Collectively, these data indicated the multifaceted variations of MAIT cells in gene expression profiling and immune response in patients with NASH.

Identification of candidate genes in MAIT cells for clinical diagnosis of NASH

Having illuminated the gene expression pattern of MAIT cells between the indicated groups, we hypothesized the feasibility of candidate genes for NASH diagnosis. For the purpose, a total number of top 26 DEGs associated with MAIT cells were specifically enriched according to log2 (P value) and the network of them was available according to PPI diagram (Fig. 5A-5B). Meanwhile, we turned to the online database and screened 9 candidate biomarkers in MAIT cells (Fig. 5C-5D). As shown by the Venn Map diagram, a total number 9 genes were enriched by overlapping them with the indicated 18 DEGs, and 4 candidate genes (GADD45B, STAT1, CCL4, and RPL38) were further selected by overlapping with the representative DEGs in MAIT cells (Fig. 4C, Fig. 5E).

Subsequently, we detected the mRNA expression of the aforementioned candidates in total T cells of PBMCs by qRT-PCR analysis, and found that the expression of GADD45B was significantly declined in the NASH group, whereas STAT1, CCL4 and RPL38 were collectively upregulated instead (Fig. 5F). Meanwhile, we conducted immunofluorescent staining of liver tissue for further identification, and found that the expression level of GADD45 was higher in CD161⁺ MAIT cells of liver tissues in patients with NASH over the HC group, whereas the relative candidate biomarkers (STAT-1, CCL4, RPL38) revealed a reverse expression pattern (Fig. 1E-1H). Taken together, these findings indicated MAIT cells and the concomitant genes served as potential candidate biomarkers of NASH.

STAT1-T-bet axis mediated the differentiation process and immunodysfunction of MAIT cells in NASH

STAT1 and STAT3 has been indicated respectively playing a pivotal role in the obesity of NASH patients and HCC in human and mice[32, 33], and we also observed the accumulation and upregulation of STAT1 in total T cells of PBMCs and liver tissue, yet the detailed information in MAIT cell-related pathogenesis of NASH was largely obscure. For the purpose, we detected the expression of STAT1-related genes in MAIT cells, and in particular, RORγt and GATA3 were reported with a pivotal role in mediating the activation and anti-inflammatory response of MAIT cells. With the aid of qRT-PCR and western-blotting analyses, we found that T-bet and RORγt were sharply upregulated in the NASH group, whereas RUNX1 and GATA3 were declined compared to the HC group (Fig. 6A-6C). Simultaneously, we assessed diverse MAIT cell-associated cytokines in the peripheral blood plasma, and verified that the concentrations of GzmB and pro-inflammatory factors (IFN-γ, IL-12, IL-17, IL-22) were consistently increased in patients with NASH (Fig. 6D). Overall, our findings indicated that STAT1-T-bet axis played a facilitating role in the activation of MAIT cells and the secretion of diverse pro-inflammatory effectors in NASH (Fig. 6E).

Longitudinal studies have indicated NASH as a necro-inflammatory response and progressive form of liver disease that commonly deteriorates into liver cirrhosis and even accelerates hepatocellular carcinoma (HCC), which is related with disease progression, cirrhosis development, and needs for liver transplant[3, 34, 35]. For decades, we and other investigator have put forward the medical diagnosis and pathogenesis of NASH such as microbiome, metabolite, cellular interactome, genes and noncoding RNAs and cellular immune abnormality[20, 21, 36]. However, the systematic and detailed information of MAIT cells during NASH are largely obscure. In this study, we took advantage of scRNA-SEQ and biofunctional analyses, and verified that patients with NASH revealed diversity in immune cell spectrum, and in particular, MAIT cells were upregulated in PBMCs and exhibited multifaceted variations in subclusters and single-cell transcriptomic signatures comparted to healthy donors. Collectively, these data indicated the involvement of MAIT cells in the pathogenesis of NASH and would benefit the further development of diagnostic biomarkers and targeted therapy in future.

State-of-the-art literatures have highlighted the pivotal role of dysimmunity in the pathogenesis of diverse hepatic diseases. For instance, Diedrich et al reported the decrease of intrahepatic lymphocytes (IHL) in patients with non-alcoholic fatty liver disease (NAFLD), including T cells (CD3⁺, CD8⁺, MAIT cells) and CD56^dim NK cells in PBMCs[37]. Instead, Naimimohasses et al verified the influence of dietary and exercise intervention upon intrahepatic MAIT cells in NAFLD patients[38]. Of note, Gebru et al andWaller et al put forward that diverse T cell subsets (γδ T cells, NKT cells, MAIT cells) and NK cells participated in the disease progress of NAFLD[39]. Taken together, these findings suggested the immune cell spectrum and the concomitant immune response in NAFLD. In this study, by conducting multi-dimensional bioinformatics analyses, we further identified the alterations in the content and unique subclusters of MAIT cells between HC and NASH. In consistent with the intrahepatic MAIT cells (liver portal tract, liver lobular area), the circulating MAIT cells in peripheral blood revealed multifaceted dysfunction and omics alterations in NASH. Interestingly, with the aid of two mouse models of chronic liver injury, Hegde et al found the accumulation of hepatic fibrogenic cells and the increase in liver fibrosis of MAIT cell-enriched mice, whereas the MAIT cell-deficient mice showed sustained resistance, which collectively highlighted the profibrogenic effect of MAIT cells during chronic liver injury[17]. Taken together, these data in combination with the indicated biomarkers in serum and excreta (e.g., lncPRYP4-3, RPS4Y2) would benefit the further exploration of noninvasive diagnosis and targeted regimens of NASH in future[20, 21].

Signal transducer and activator of transcription 1 (STAT1) belongs to the STAT protein family and mediates cellular responses to the Janus kinase (JAK)-signal, interferons (e.g., IFN-γ), IFN-stimulated genes (ISG) and relative modulators (e.g., KITLG/SCF, IRF-9), and the dysfunction of STAT1 commonly causes diverse immune dysregulation phenotypes[40–43]. For example, Grohmann et al put forward contribution of obesity- associated hepatic oxidative stress upon the pathogenesis of NASH in C57BL/6 mice via increasing STAT1/STAT3 signaling and inactivating the T cell protein tyrosine phosphatase (TCPTP)[32]. Instead, STAT1 deficiency has been recognized as a pro-inflammatory imprint of the naive CD4⁺ T cell population in spondyloarthritis[44]. Herein, our data further indicated the role of STAT1-mediated signaling in NASH via modulating the hyperactivation and differentiation as well as the pro-inflammatory effect of MAIT cells in peripheral blood. Notably, Zhang et al verified the feasibility of intervening malignant progression in gastric cancer via regulating the ubiquitination and degradation of STAT1[45]. As to NASH, the hyperactivation of hepatic stellate cells (HSCs) has been considered as the core event during fibrosis progression, which could be orchestrated by the imbalance of immune cells (e.g., Th17 cells, double negative T cells, pro-inflammatory macrophages), accumulation of pro-inflammatory cytokines (e.g., IL-17A, TNFα, IFN-γ) and even the mitochondrial dynamics-related hepatocyte epithelial mesenchymal transition (EMT)[46, 47]. Therewith, it’s of great interesting to further illuminate the STAT1-related regulatory mechanism of NASH and novel targeted therapy in future.

Ethics approval and consent to participate

All patients and healthy donors involved in this study have signed informed consents. Meanwhile, this study was approved by the Ethical Committee of The First Affiliated Hospital of Kunming Medical University (approval no.: 2020-L-08) according to the principle of Declaration of Helsinki.

Consent for publication

Not applicable.

Availability of data and material

The data analyzed during this study and the additional files have been included in the published article. The datasets in the study are available from the corresponding author on reasonable request.

Competing interests

The authors declare there’s no competing interests and all authors consent to publish the data.

Funding

This work was supported by The Famous Medical Specialist of “High-level Talent Training Support Program” in Yunnan Province ( RLMY20190004 ), National Natural Science Foundation of China (82260031), Yunnan Science and Technology Project (L-2017018), Natural Science Foundation of Yunnan (202201AY070001-042), Postdoctoral Program of Natural Science Foundation of Gansu Province (23JRRA1319), The Innovative Team of Yunnan Province (202305AS350019), Clinical Research Center for Geriatric Diseases (202102AA310069), Gansu Provincial Hospital Intra-Hospital Research Fund Project (22GSSYB-6), The 2022 Master/Doctor/Postdoctoral program of NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor (NHCDP2022004, NHCDP2022008).

Authors’ contributions

Jing Xu and Xingjie You: designed and performed the experiments, collection and assembly of data, manuscript writing; Shixin Huang, Fenglin Xue, Tangwei Mou, Zihan Wu, Ao Wang, Ao Wang, Qiu Qu, Man Gu, and Ting Fang: helped with collection and assembly of data; Jiajia Yin, Qiquan Mo, Huiping He, Linran Zeng, Yu Yang, and Yongli Wang: data analysis and interpretation, manuscript writing; Leisheng Zhang, Yang Sun，Hanfei Huang, and Hongju Yang: conception and design, revision, final approval of manuscript. All the co-authors have read and approved the final manuscript.

Acknowledgements

The coauthors thank the doctors and nurses in The First Affiliated Hospital of Kunming Medical University, The Fourth People’s Hospital of Jinan (The Third Affiliated Hospital of Shandong First Medical University). We also thank LC-Biotech for their technical support.

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No competing interests reported.

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Uncovering the Single-cell Transcriptomic Signatures and Pathogenesis of Mucosal-associated Invariant T cells during Nonalcoholic Steatohepatitis

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Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Patient enrollment and specimen collection

Preparation of Peripheral blood mononuclear cells (PBMCs)

Western-blotting analysis

Total RNA extraction and quantitative real-time PCR (qRT-PCR) analysis

Flow cytometry (FCM) assay

Single-cell RNA-sequencing (scRNA-SEQ)

Bioinformatics analyses of scRNA-SEQ data

Pseudo-time trajectory analysis

Hematoxylin and eosin (H&E) staining

Statistical analysis

Results

The landscape of immune cell subtypes and decline in T cells in NASH

The T cell spectrum and sharp increase in MAIT cells in NASH

The alterations in the differentiation trajectory of MAIT cells in NASH

Characterization of the transcriptomic signature of MAIT cells in NASH

Identification of candidate genes in MAIT cells for clinical diagnosis of NASH

STAT1-T-bet axis mediated the differentiation process and immunodysfunction of MAIT cells in NASH

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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