CD16+ Monocytes Are Involved In The Hyper-inflammatory State Of Prader-Willi Syndrome By Single-cell Transcriptomic Analysis

BackgroundPrader-Willi syndrome(PWS)is a rare,complex,multisystem syndrome with an estimated prevalence of 1 in 10,000-30,000 live births,and it was first reported by Prader in 1956,Genetically,PWS is an imprinted disease caused by the lack of 5-6Mb active genes located in the paternal chromosome 15q11-q13 region.The absence of gene expression in this region mainly occurs through three mechanisms:paternal deletion of the 15q11-ql3 region(65-75%),maternal uniparental disomy(20-30%),or imprinting defects(1-3%).The syndrome exhibits a wide clinical presentation spectrum,including hypotonia,developmental delays,cognitive disability,psychiatric phenotypes,sleep-disordered breathing and obesity.Chronic inflammation refers to a low-level,persistent non-infectious inflammatory state characterized by abnormal activation of immune cells.Chronic inflammation-related diseases caused by systemic chronic inflammation,such as cardiovascular diseases,type 2 diabetes mellitus(T2DM),and psychiatric disorders,have been recognized as the major causes of death in the world.Multiple studies have indicated that individuals with PWS had a higher susceptibility to inflammation-related diseases compared to populations matched for body mass index(BMI).This increased risk was recognized as one of the leading causes of mortality among PWS patients.Alice Clerc reported that type T2DM was common in PWS and affected 20%of adults compared to 5-7%of the general population.More recently,a Japanese team and a Korean team both found more than 50%of PWS patients have developed diabetes before the age of 18.Additionally,a variety of psychiatric disorders are common in PWS.Clinical studies have found that more than 20%of PWS patients are diagnosed with severe depression,and 16%to 32%experience episodes of psychosis.Furthermore,research has shown an increased risk of atherosclerosis in PWS,with an onset at a younger age.Some studies demonstrated that PWS was associated with increased concentrations of circulating markers of inflammation,such as interleukin-6(IL-6),interleukin-1β(IL-1β),tumor necrosis factor(TNF),and C-reactive protein(CRP)compared to those with non-syndromic obesity.There have been many reports that cytokines mentioned above such as IL-1β and TNF play a central role in mediating T2DM,cardiovascular diseases,and progression of mental disorders.Another study has found that serum levels of IL-1β and interleukin-13(IL-13)in PWS patients are associated with the severity of various psychiatric symptoms.In PWS patients,circulating inflammatory markers in the peripheral circulation are also correlated with specific markers of immune cell activation.It is proposed that the peripheral immune system is activated in PWS,which leads to systemic inflammation manifested by increased cytokine levels and seems to play a critical pathogenic role in the development of these inflammation-related comorbidities.This motivates us to shift our own attention to changes in PWS peripheral immune cells.Due to the significant heterogeneity and complex functionality of peripheral immune cells,the detailed characteristics of peripheral immune cells in PWS have not been fully elucidated.Single-cell RNA sequencing(scRNA-seq)provides an unbiased and comprehensive approach to defining cell types and states based on the transcriptional features of individual cells.It has been widely used to unravel the heterogeneity and diversity of immune cells and identify disease-associated cellular subpopulations.In this study,we employed scRNA-seq technology for the first time to construct a peripheral immune cell atlas in PWS patients.Our aim is to identify key pathogenic subpopulations in PWS and further validate our findings via mass cytometry(CyTOF).The present study offers valuable theoretical support for future investigations and therapeutic approaches focused on modulating the immune system in individuals diagnosed with PWS.Objectives1.High-throughput multiplex assay was applied to determine the changes of serum cytokines in the PWS.2.To investigate the correlation between serum cytokines and peripheral immune cells in PWS.3.Construction of a peripheral immune cell atlas in PWS using scRNA-seq,with the objective of elucidating the characteristics of these cells and their roles in the hyper-inflammatory state of PWS.4.A thorough investigation was carried out to identify the cell subpopulation closely associated with the PWS and revealed the specific genes,gene function enrichment,and interactions with other cell clusters for the subpopulation.5.To explore the relationship between the 15q11-q13 region and the elevated levels of inflammation in PWS.Methods(一)Subject enrollmentThis study included individuals with PWS and healthy controls.All study participants were recruited from Shandong Provincial Hospital affiliated with Shandong First Medical University between 2019 and 2021.Written informed consent was obtained from parents or legal guardians.The diagnosis of PWS was confirmed through genetic testing,and all subjects did not take any medication.All participants had no history of cancer,autoimmune diseases,diabetes,infections,or steroid use.Age,gender,height,weight,and medical information were recorded for all study subjects,and PWS patients underwent methylation sensitive multiplex ligation dependent probe amplification(MS-MLPA)testing.All participants were instructed to fast after midnight,and fasting blood samples were collected the next morning between 8-10 am.(二)Multi-omics detection and analysis1.Multiplex-cytokine array detection and IL-1β detectionSerum cytokines were measured for each subject using a 65-multiplex cytokine assay(Cat.No.EPX650-16500-901)on the Luminex 200 system.IL-1β levels were measured using high-sensitivity ELISA kits by R&D Systems(Catalog Number HSLB00D)following the instructions exactly.2.Single-cell transcriptome sequencing and analysis2.1 Extraction of peripheral blood mononuclear cell(PBMC):The PBMCs were isolated within 2 hours from fresh EDTA anticoagulated whole blood by density gradient centrifugation using Histopaque-1077(Sigma,A6929).2.2 Single-cell cDNA library construction and sequencing:The single-cell library preparation in our research relied on an available droplet method,the 10x Genomics Chromium Controller.Single-cell RNA-seq libraries were constructed using the Chromium Single Cell 3’ Library&Gel Bead Kit v3·1(10x Genomics,Pleasanton,CA)according to the manufacturer’s instructions.2.3 Single-cell transcriptome profiles were performed:After unbiased clustering analysis via Seurat R package version 4.1.0,data were then visualized by UMAP.The cellular populations in PBMCs were identified using well-known marker genes.2.4 Differentially expressed genes(DEGs)analysis and Functional enrichment analysis:Based on the Wilcoxon-test method implemented in the FindAllMarkers function of the Seurat package,we analyzed DEGs between two groups.To find the function of DEGs,we used the function clusterProfiler to examine potential biological functions for lists of genes.2.5 Constructing single-cell pseudotime differentiation trajectory:To interpret cell differentiation fate decisions,we used Monocle(v 2.10.0)to order single cells along pseudotime according to the official tutorial to explore the process of cell differentiation and identify genes that can potentially determine cell fate.2.6 Cell-cell communication:Cell-cell interactions analysis was conducted using CellChat(version 1.1.2).On the basis of data from scRNA-seq,the CellChat analysis was performed to infer intercellular and intracellular crosstalk between assigned types of cells.2.7 Clinical parameters and module eigengenes:The WGCNA package(version 1.70)was utilized to generate modules for co-expression.To identify modules correlated with clinical traits,Spearman’s rank correlation coefficients were measured between the different clinical parameters and module eigengenes.3 Mass cytometry3.1 Metal-labeled antibodies:All the antibodies were purchased from BioLegend.According to Fluidigm’s recommendations,antibody conjugations were prepared using the Maxpar Antibody Labeling Kit(Fluidigm,South San Francisco,CA).3.2 Staining protocol:PBMCs were spun and resuspended and incubated in 1 mL of 5 μM cisplatin(Fluidigm)at room temperature.Then PBMCs were incubated with metal-labeled antibodies followed by Ir-intercalator staining.3.3 Data analysis:We used Cytobank software to gate the output FCS files to rule out fragments,dead cells,and doublets.Finally,data were clustered and represented in t-SNE maps using the R package Cytofkit.4 Bulk transcriptome analysis of PWS-related models.Using the GEO public database,transcriptional data related to PWS models was downloaded.The Limma software was employed to perform differential gene analysis,and functional enrichment analysis was conducted on the DEGs.(三)Statistical analysesThe data were analyzed using SPSS 22.0 or R 4.1.0.The distribution normality was tested using the Shapiro-Wilk test normality test.The Mann-Whitney rank-sum test was used for data with non-normal distribution and the t-test was used for data with normal distribution.Categorical variables were compared by χ2 or Fisher’s exact tests.Results1.Serum proinflammatory cytokine levels were elevated in PWSTo determine typical proinflammatory cytokines levels in PWS patients,the cytokines of 10 PWS patients and 13 controls were measured using a 65-multiplex cytokine assay.19 of the 35 detectable indicators(54%)in the PWS group were 1.2-fold higher relative to the control group.Most of the increased indicators in PWS were proinflammatory cytokines and chemokines,including TNF-RII,IL-12p70,MIF,MIP-1α and TNF-α.We re-detected IL-1β with Elisa and found in line with previous reports,higher levels of IL-1β were observed in PWS patients compared to the controls.2.Elevated levels of proinflammatory cytokine had no correlation with BMI in the PWS patientsThe difference in serum cytokine levels was still significant when the PWS and control groups had comparable BMIs.In addition,correlation analysis between serum IL-1β and other indicators showed no statistically significant association with BMI in the PWS group indicating obesity not may be responsible for the increased levels of inflammatory serum markers.3.An atlas of peripheral immune cells in PWSDroplet-based scRNA-seq technology was used to profile PBMCs derived from 6 patients with PWS and 12 healthy controls.We analyzed a total of 96,067(control:74,457;PWS:21,610)cells in all participants after stringent filtering of the scRNA-seq data,with an average 6,754 UMIs per cell and 2,037 genes per cell.We identified six major immune cell lineages including CD8+T cells,CD4+T cells,gamma delta T cells(gd T),natural killer cells(NK),B cells(BC),and monocytes(Mon).4.PWS peripheral immune cells were in a hyper-inflammatory state,independent of obesityGlobally,we found inflammatory cells increased by approximately 100%in all PBMCs using scRNA-seq analysis.The upregulated DEGs in PWS were primarily inflammation-related genes and the generally upregulated genes across major cell types were enriched in inflammatory-related pathways,such as the inflammatory response pathway,NFkB signaling pathway,production of inflammatory cytokines,and stress-related pathways.Hierarchical clustering and WGCNA results indicated that the high inflammatory state of PBMCs was not associated with BMI.5.Monocytes were closely related to the hyper-inflammatory state of PWSThe analysis of scRNA-seq data revealed that monocytes were the primary source of inflammatory cells in PWS patients.Besides,monocytes exhibited the most significant changes in gene expression.In the PWS group,we also observed a positive correlation between the transcription levels of pro-inflammatory cytokines in monocytes and the corresponding levels of cytokines in the serum.These results indicate a close association between monocytes and elevated levels of inflammation in PWS.6.CD16+monocyte increased in the PWS groupTo describe how cell-type composition changed in the PWS of monocytes,we separately compared the percentage of each cluster between the PWS and control groups.CD 16+monocytes exhibited the greatest changes and increased by approximately 100%based on the scRNA-seq analysis.The result was confirmed by CyTOF.7.Expression of genes associated with CD16+monocyte differentiation were increased in PWSThe development of peripheral circulating monocytes is currently not well understood.To elucidate this process,we performed pseudotime trajectory analysis of monocytes using Monocle2.The results showed that the key genes involved in controlling the differentiation of CD 16+monocytes were NR4A1 and CEBPB,and these two genes were both upregulated in PWS.8.CD16+monocyte exhibited a hyperinflammatory phenotype in PWSA large number of upregulated genes with inflammation-related functions were observed in the CD 16+monocytes in PWS.To further understand the biological functions of these genes,we examined the upregulated DEGs by gene set enrichment analysis using cytokine-responsive gene sets from cytokine-treated cells(LINCS L1000).PWS-upregulated DEGs were enriched by TNF/IL-1β-responsive genes and the result was confirmed by CyTOF.Further analysis revealed a significant increase in genes and pathways related to cellular senescence in PWS.Additionally,PWS patients exhibited significantly shortened telomeres,indicating that the aging of CD 16+monocytes may be a potential underlying cause of the heightened inflammatory state observed in CD 16+monocytes in PWS patients.9.CD16+monocytes regulated the inflammatory process of other cells though cell-cell interactionsCellchat results indicated that CD 16+monocytes in the PWS group showed greater changes in transmitted and received signaling compared to those in the control group which implied that CD 16+monocytes may have an increased tendency for interaction with other immune cells in blood vessels.Inflammatory cytokines/receptors constitute the major communication network between CD16+monocytes and other immune cells which suggests that CD 16+monocytes may transmit inflammatory signals to other cells,contributing to the overall inflammation of peripheral immune cells in PWS.10.The 15q11-q13 region plays a critical role in regulating the peripheral immune cells’ inflammationTo assess the impact of the 15q11-q13 region on circulating immune cells,we selected the genes of the 15q11-q13 region and calculated 15q11-113 gene scores in each cell using AUCell to evaluate gene expression in the 15q11-q13 region in the healthy group.15q11-q13 gene scores were associated with inflammation scores in healthy individuals after adjusting for the relevant confounders such as BMI and age.The regression analysis revealed that 32.6%of the variance in inflammation scores was explained by 15q11-q13 gene scores.Mediation analysis revealed that the lower 15q11-q13 gene expression observed in healthy individuals with a tendency toward higher inflammation score may be explained at least in part by high CD 16+monocyte levels.Conclusions1.Serum proinflammatory cytokine levels were elevated in PWS.2.PWS peripheral immune cells were in a hyper-inflammatory state and monocytes were the most pronounced.3.CD 16+monocytes not only increased in number but also elevated the inflammatory levels in PWS by altering their cellular states.Cell communication revealed that CD 16+monocytes promoted the inflammatory processes in other cell types through the transmission of inflammatory signals,potentially serving as a crucial pathogenic subset in PWS.4.The 15q11-q13 region plays a critical role in regulating the peripheral immune cells inflammation.