Bulk and Single-Cell Transcriptome Analysis Reveal Shared Key Genes and Patterns of Immune Dysregulation in Sepsis and Systemic Lupus Erythematosus

doi:10.21203/rs.3.rs-3113873/v1

Download PDF

Research Article

Bulk and Single-Cell Transcriptome Analysis Reveal Shared Key Genes and Patterns of Immune Dysregulation in Sepsis and Systemic Lupus Erythematosus

https://doi.org/10.21203/rs.3.rs-3113873/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by autoantibody production and multi-organ dysfunction. SLE patients are at an elevated risk of sepsis due to immune dysregulation. Sepsis is a life-threatening condition resulting from dysregulated responses to infection and is the leading cause of ICU admission and death in SLE patients. However, the common mechanism of immune dysregulation shared between these two diseases still remains unclear.

Methods: Sepsis and SLE datasets were harvested from the Gene Expression Omnibus and Single Cell Portal database. Differential expression and weighted gene co-expression network analysis (WGCNA) were used to identify essential hub genes crucial to sepsis and SLE. The least absolute shrinkage and selection operator (LASSO) regression was used to establish a diagnostic model for SLE, and a receiver operating characteristic (ROC) curve was performed to assess the diagnostic efficiency of the model for SLE and individual genes for sepsis. Single-cell RNA sequencing data of PBMCs from patients with sepsis or SLE were analyzed to evaluate the proportion of different immune cell types. The expression profiles of hub genes in sepsis and SLE patients were further investigated, and similar pathway changes were explored by Gene Set Enrichment Analysis and Gene Ontology (GO) enrichment analysis.

Results: We identified 49 co-upregulated and 44 co-downregulated genes between sepsis and SLE datasets. GO analyses of these differentially expressed genes (DEGs) showed that they mainly affected the defence response to the bacteria and immune response. Combined DEGs and WGCNA analysis, we identified 11 hub genes: ANKRD22, RSPH9, DHRS9, AIM2, CCNA1, CEACAM1, FBXO6, TNFAIP6, FCGR1A, PLSCR1, and FCGR1BP. LASSO regression analysis and ROC curve highlighted TNFAIP6 and PLSCR1 as key genes with strong diagnostic values for sepsis and SLE. Single-cell RNA analysis showed an elevated proportion of CD14⁺ monocytes in sepsis and SLE patients, and hub gene expression was significantly increased in this group. Meanwhile, CD14⁺ monocytes in these two diseases shared some common transcriptional changes.

Conclusion:

TNFAIP6 and PLSCR1 are essential genes with strong diagnostic values for sepsis and SLE. In addition, the proportion of CD14⁺ monocytes within PBMCs of sepsis and SLE patients increased, with indications of some shared transcriptional changes.

Sepsis

Systemic lupus erythematosus

PLSCR1

Single-cell transcriptome

CD14+ monocyte

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by autoantibody production and multiorgan damage[1]. SLE can involve multiple vital organs, such as the kidney, heart, and brain, resulting in lupus nephritis, pericarditis, and lupus encephalopathy, thus significantly increasing patient mortality [1, 2]. SLE predominantly impacts young women, with an incidence ranging from 1.5 to 11 per 100,000 people worldwide [3]. Genetic and environmental factors contribute to the pathogenesis of SLE. Its primary pathogenesis involves persistent activation of the type I interferon signalling pathway, innate and adaptive immune response dysfunction, abnormal activation of B and T lymphocytes, and the production of autoantibodies and inflammatory mediators [4–6]. Currently, the main therapeutic methods for SLE are glucocorticoids, antimalarial drugs, and immunosuppressive agents [7]. With continuous improvement in diagnosis and treatment, the mortality of SLE has significantly decreased in recent years [8, 9]. However, long-term use of glucocorticoids and immunosuppressants, combined with autoimmune dysfunction, leads to a high risk of infection in some SLE patients [10]. A retrospective study revealed that infection is the leading cause of death in SLE patients, and mixed infections are more prevalent than single-pathogen infections [11]. Similarly, Oud found that sepsis is the main reason for ICU admission in SLE patients, significantly increasing the patient’s mortality [12].

Sepsis is a condition that arises from dysregulated immune responses to infection, leading to severe organ dysfunction and potentially life-threatening circumstances [13]. Patients with sepsis often require ICU admission for advanced support and treatment [14]. SLE patients are particularly susceptible to infection due to complement deficiencies and mannose-binding lectin [10, 15]. Furthermore, when lupus impacts the blood system, patients can develop leukopenia, including reductions in neutrophils or lymphocytes [16]. Additionally, anti-red blood cell and anti-platelet antibodies can lead to anaemia and thrombocytopenia. These factors collectively compromise the immunity of SLE patients, making them more susceptible to sepsis following bacterial, fungal, or viral infections than the general population. Notably, both SLE and sepsis share the complex challenge of immune dysfunction, yet their potential correlations remain largely uncharted. Given this, it becomes vital to deepen our understanding of the common alterations of immune cells and genes between sepsis and SLE to improve long-term prognoses for SLE patients. By comprehensively decoding the shared modifications between these two diseases and identifying potential diagnostic biomarkers, we could establish a foundation for more efficacious therapeutic strategies, ultimately improving clinical outcomes.

In our study, we obtained six datasets for sepsis and SLE from the Gene Expression Omnibus (GEO) and Single Cell Portal (SCP) databases. Then, we identified differentially expressed genes (DEGs) between patient groups and normal volunteers, uncovering common DEGs. Through weighted gene co-expression network analysis (WGCNA), we discovered vital module genes correlating positively with both diseases. Intersecting common DEGs and key modules revealed 11 hub genes. Subsequently, crucial diagnostic biomarkers were determined via least absolute shrinkage and selection operator (LASSO) and receiver operating characteristic (ROC) curve analysis. Furthermore, after conducting separate single-cell analyses on the sepsis and SLE datasets, we discerned a notable increase in the proportion of CD14⁺ monocytes in both conditions. Of particular interest, the 11 hub genes primarily manifested expression in CD14⁺ monocytes, and these cells presented some common transcriptional changes in both diseases.

Data Collection and Processing

We sourced two raw Bulk RNA datasets (GSE95233 [17], GSE57065 [18]) containing gene expression data for both sepsis patients and controls, along with two bulk RNA SLE datasets (GSE61635 [19], GSE49454 [20]) that include data for both SLE patients and healthy controls from the GEO (https://www.ncbi.nlm.nih.gov/geo/) database. In addition, we have examined the single-cell RNA sequence dataset GSE135779, which comprises 40 SLE samples and 16 healthy controls [21]. Furthermore, we have looked into the single-cell dataset SCP548 from The Broad Institute Single Cell Portal (https://singlecell.broadinstitute.org/single_cell/study/SCP548/), which includes 29 sepsis samples and 19 healthy controls. The flow chart of this study is shown in Fig. 1. The information on sepsis and SLE patient datasets is exhibited in Table 1.

Table 1

The source information of sepsis and SLE datasets.
Accession ID	Platform	Tissue	Total	Patient	health volunteer
Sepsis
GSE95233	GPL570	Peripheral Blood	124	102	22
GSE57065	GPL570	Peripheral Blood	107	82	25
SCP548	Unknown	PBMC	48	29	19
SLE
GSE61635	GPL570	Peripheral Blood	129	99	30
GSE49454	GPL10558	Peripheral Blood	177	157	20
GSE135779	GPL20301	PBMC	56	40	16

Identification of DEGs

We obtained the normalised gene count tables from the GSE95233, GSE57065, GSE61635, and GSE49454 datasets. The probe IDs of the microarray were converted into gene symbols using the “tinyarray” package in R studio. With the aid of the 'limma' package in R studio, differential expression analysis was conducted on GSE95233 for sepsis versus healthy controls and GSE61635 for SLE versus healthy controls [23]. Genes with |log2 Fold change| > 1 and adjusted P-value < 0.05 were considered as differentially expressed.

Gene Ontology (GO) Analysis

The common DEGs were converted to gene IDs using the R package “org.Hs.eg.db”. Analyses of GO for DEGs were performed by the R package “clusterProfiler” [24]. The significantly different GO terms and signal pathways were screened by the threshold p-value < 0.05. The top five enriched terms from the Biological Process (BP) and Cellular Component (CC) categories, as well as the sole enriched term from the Molecular Function (MF) category, were visualised using the 'enrichplot' and 'ggplot2' packages in R.

WGCNA

WGCNA was performed on the GSE95233 and GSE61635 datasets using the “WGCNA” package in R software to identify correlated gene modules in sepsis and SLE. During the WGCNA analysis of GSE61635, three outlier specimens (GSM1509664, GSM1509669, and GSM1509698) were identified and excluded due to their extreme expression profiles. A co-expression network was then constructed by setting a soft threshold power to ensure a high scale-free topology fitting index (R^2) and mean connectivity among the genes. The minimum number of gene modules in the dynamic tree cut and module identification section was set to 100. Clinical characteristic data for sepsis and SLE samples were obtained from the GSE95233 and GSE61635 datasets, and the relationship between gene modules and clinical traits was visualised in a heatmap.

LASSO Regression

To evaluate the efficacy of the 11 pre-selected genes in diagnosing SLE, we employed LASSO regression using the “glmnet” package in R [25]. We fit the LASSO model to the training dataset GSE61635 and performed 5-fold cross-validation using the cv. glmnet function to determine the optimal tuning parameter (lambda) that minimizes the mean cross-validated error. The final LASSO model included two of the 11 genes with non-zero coefficients, which were considered as important predictors of SLE disease status based on the model.

ROC Curve

To assess the performance of the LASSO model in diagnosing SLE, ROC curve analyses were conducted on gene expression data from GSE61635 and GSE49454 datasets using the pROC package [26]. Simultaneously, ROC curve analyses were performed on GSE95233 and GSE57065 datasets to evaluate the diagnostic value of key genes in sepsis. The area under the curve (AUC) was utilized to compare the discriminative abilities of the LASSO models in diagnosing SLE and the individual genes in diagnosing sepsis.

Analysis of Single-Cell RNA Sequence Data

Criteria applied to the GSE135779 dataset included: number of feature RNA between 200 and 2500, UMI count ≥ 400, and mitochondrial gene percentage < 0.2. The following criteria were applied to the SCP548 dataset: number of feature RNA between 500 and 3000, UMI count > 500, and mitochondrial gene percentage < 0.15. And The Seurat software package (version 4.3.0, https://satijalab.org/seurat/) was employed to normalise the expression matrix and obtain scaled data. The principal component analysis (PCA) was constructed using the top 2,000 highly variable genes for sample integration via the harmony package [27]. Construction of Uniform Manifold Approximation and Projection (UMAP) was based on the top 30 harmony components. The UCell package was utilized to calculate the enrichment score of the hub genes across all cells [28]. We performed a differential expression analysis with the FindMarkers function in Seurat to identify the DEGs in different disease states. We screened for DEGs using the following thresholds: |average fold change| ≥ 0.25, P-value ≤ 0.05.

Functional Enrichment Analysis of Key Genes

To elucidate the functions of DEGs identified in monocytes from sepsis or SLE patients compared to healthy controls, we performed Gene Set Enrichment Analysis (GSEA) and GO analysis. Hallmark pathways with significant enrichment were ranked and visualized based on Net Enrichment Score (NES), gene counts, and P-value. The common DEGs identified in CD14 + monocytes derived from sepsis and SLE datasets were subjected to GO term enrichment using the clusterProfiler package. The enriched GO terms were clustered and visualized in a network using the aPEAR package.

Statistical Analysis

The statistical component of our research was conducted using the R software (version 4.2.2). Differences in gene expression among groups were statistically tested using the Wilcox test, with a P-value of less than 0.05, denoting statistical significance. The correlations within our data were evaluated using Pearson's correlation analysis.

Common DEGs Identified in Sepsis and SLE and Their GO Analysis

DEGs were extracted from GSE95233 and GSE61635 using an absolute log₂FC cut-off of 1 and an adjusted P-value cut-off of 0.05. We identified 1129 DEGs between sepsis patients and healthy individuals, with 602 genes upregulated and 527 downregulated (Fig. 2A). Similarly, in the case of SLE patients versus healthy individuals, we found 805 DEGs, of which 543 were upregulated, and 262 were downregulated (Fig. 2B). Common upregulated and downregulated genes between the sepsis and SLE datasets were identified, resulting in 49 co-upregulated and 44 co-downregulated genes (Fig. 2C). We proceeded with GO functional enrichment analysis to better understand the potential functional implications of these DEGs. The top five most significantly enriched GO categories are depicted in a bar chart, ranked by p-value. In the BP category, enrichment was primarily related to defence against infection and immune regulation. The CC category was associated mainly with granules. The only significantly enriched term for MF was immunoglobulin binding (Fig. 2D).

Identify the module most positively associated with the diseases

We constructed a scale-free topology network using a soft threshold 8 for GSE95233 (Supplementary Fig. 1A). Similarly, we set a soft threshold 10 for GSE61635 to build a scale-free network (Supplementary Fig. 1B). Following this, a gene cluster tree was generated for both datasets, using hierarchical clustering analysis based on adjacency value differences. This allowed identifying modules containing a minimum of 100 genes (Fig. 3A, B). Subsequently, a heatmap was generated, in which Pearson correlation coefficients were used to elucidate the relationships between the identified modules and disease states. In the WGCNA analysis of GSE95233, the turquoise module emerged as having the most potent positive correlation with sepsis (r = 0.86, p = 2e-37, genes = 2229) (Fig. 3C). And we demonstrated the highest positive association with SLE in the WGCNA analysis of GSE61635, the red module (r = 0.75, p = 4e-24, genes = 386) (Fig. 3D).

Significant Diagnostic Value of TNFAIP6 and PLSCR1 in Sepsis and SLE

To determine the most critical genes associated with sepsis and SLE, we intersected their common DEGs with the sepsis-related and SLE-related modules (Fig. 4A). This approach revealed 11 genes: ANKRD22, RSPH9, DHRS9, AIM2, CCNA1, CEACAM1, FBXO6, TNFAIP6, FCGR1A, PLSCR1, and FCGR1BP. These genes were significantly upregulated in sepsis and SLE (Supplementary Fig. 4A, B). We then subjected these significant genes to LASSO regression analysis to construct a diagnostic model for SLE using the GSE61635 dataset. The model performed optimally when incorporating two genes, PLSCR1 and TNFAIP6, for SLE screening (Fig. 4B and Fig. 4C). We evaluated the model’s sensitivity and specificity for diagnosing SLE by ROC curve analysis, resulting in an AUC value of 0.978, indicating a strong SLE diagnostic value (Fig. 4D).To assess the model’s predictive ability in an external dataset, we performed ROC curve analysis using the validation SLE dataset GSE49454. The AUC value of 0.864 further demonstrated the model’s diagnostic value (Fig. 4E). Additionally, the expression of TNFAIP6 and PLSCR1 was significantly higher in SLE patients compared to healthy individuals in both the GSE61635 and GSE49454 datasets (Fig. 4F). Considering that the lasso model from the SLE datasets cannot be directly utilised in the sepsis dataset, we performed ROC curve analysis to evaluate the diagnostic potential of TNFAIP6 and PLSCR1, assessing their sensitivity and specificity for sepsis diagnosis. TNFAIP6 and PLSCR1 exhibited AUC values exceeding 0.9 (Fig. 4G-H), indicating their strong diagnostic potential for sepsis. Subsequently, we compared the expression levels of TNFAIP6 and PLSCR1 between sepsis patients and healthy individuals in the GSE95233 dataset and the validation dataset GSE57065. We found that the expression of both TNFAIP6 and PLSCR1 was significantly higher in sepsis patients across both datasets (Fig. 4I).

Increased Proportion and Hub Gene Overexpression in CD14⁺ Monocytes of SLE Patients

To explore the gene expression patterns across various cell types in peripheral blood, we utilised single-cell transcriptomic data (GSE135779) from PBMCs of SLE patients and healthy control subjects. Following the standard single-cell data analysis process, all cells clustered into 21 clusters (Supplementary Fig. 3A). Using classical markers (Supplementary Fig. 3B), we identified nine cell types (Fig. 5A). We then compared the average cell ratios between the SLE and healthy control groups. The cellular landscape of SLE patients differed significantly from that of healthy controls (Fig. 5B). Further comparison of cell proportions in each sample revealed significant differences in CD14⁺ monocytes, CD4⁺ T cells, and NK cells between SLE patients and controls, especially CD14⁺ monocytes (P < 0.001) (Fig. 5C). This underlines the differences in immune cell profiles between SLE patients and healthy individuals. We then used the UCell package to calculate the module scores consisting of 11 hub genes within each cell type and discovered that CD14⁺ monocytes from SLE patients received the highest module scores (Fig. 5D). The same trend is reflected in the average heatmap of these 11 hub genes across all cell types, emphasising the predominant expression in CD14⁺ monocytes (Fig. 5E). Hence, we concentrated on CD14⁺ monocytes for detailed analysis. By performing DEGs studies in CD14⁺ monocytes between the SLE and control groups, we identified 149 upregulated genes and 55 downregulated genes (cutoff: |average log2FC|>0.25, p < 0.05). Notably, among these DEGs, PLSCR1 and FCGR1A showed a significant upregulation in log2 Fold change and delta percent between SLE and control groups (Fig. 5F). When comparing the expression of 11 previously identified hub genes across all samples, we found that CD14⁺ monocytes of the SLE group showed higher expression than the control group (Fig. 5G). Additionally, we investigated the relationship between cumulative hub gene scores and the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) in patients with SLEDAI scores greater than zero. Our Pearson correlation analysis found a moderate correlation of R = 0.49, a statistically significant result with a p-value of 0.0057 (Fig. 5H). This compelling correlation suggests a potential link between hub gene expression in CD14⁺ monocytes and disease activity in SLE patients.

Elevated Proportion and Hub Gene Expression of CD14⁺ Monocytes in Sepsis

In the sepsis dataset SCP548, we applied the standard single-cell analysis process, which led to the clustering of all cells into 16 distinct clusters (Supplementary Fig. 4A). By utilising classical markers for cluster differentiation (Supplementary Fig. 4B), we identified seven primary cell types within the PBMCs: CD14⁺ monocytes, CD16⁺ monocytes, B cells, CD4⁺ T cells, CD8⁺ T cells, dendritic cells, and natural killer cells (Fig. 6A). Following this, we calculated and compared the average cell ratios between the sepsis and healthy control groups. Notably, CD14 + monocytes constituted a significantly higher proportion of sepsis patients than healthy controls (Fig. 6B). Further examination of cell proportions within each sample highlighted that, in sepsis patients compared to healthy controls, the ratio of CD14⁺ monocytes was prominently increased. In contrast, the proportions of CD4 + T cells and dendritic cells were noticeably lower (Fig. 6C). Parallel to the strategy employed in our SLE dataset, we utilized the UCell package to quantify the scores of an 11-hub gene module within each cell. Insights from UMAP visualization identified CD14⁺ monocytes as the cell type achieving the peak module score. More importantly, compared to the control group, CD14 + monocytes from the sepsis group registered a conspicuously increased module score (Fig. 6D). Additionally, a subsequent heatmap representation of the average expression of the hub genes across different cell types reinforced the finding that CD14⁺ monocytes demonstrated elevated expression of most hub genes, with CCNA1, TNFAIP6, FGR1A, and FCGR1B being particularly prominent (Fig. 6E). Our subsequent differential expression analysis identified 179 upregulated genes and 128 downregulated genes within CD14⁺ monocytes when comparing sepsis patients to healthy controls (cut-off criteria: |average log2FC|>0.25, p < 0.05). Among these differentially expressed genes, FCGR1A and FCGR1B were significantly upregulated (Fig. 6F). Moreover, a comparative review of hub gene expression throughout all samples demonstrated that the sepsis group manifested comparatively higher expression levels than the control group (Supplementary Fig. 4C).

Parallel Pathway Alterations in CD14⁺ Monocytes of Sepsis and SLE

Given the significant increase in the CD14⁺ monocyte ratio and high expression of hub genes in both sepsis and SLE, we conducted a comprehensive analysis to ascertain whether CD14⁺ monocytes under these two disease conditions exhibit similar transcriptional alterations. Enrichment results from SLE monocytes underscored five significantly activated hallmark gene sets, with only one gene set demonstrating suppression (Supplementary Fig. 5A). Notably, the Interferon Alpha Response and Interferon Gamma Response pathways emerged as predominantly activated, evidenced by their Normalized Enrichment Scores (NES) of 2.38 and respective adjusted p-values of < 0.001. In contrast, the Myc Targets V1 gene set was the sole suppressed pathway, presenting an NES of -1.73 and an adjusted p-value of < 0.001 (Fig. 7A). In the sepsis GSEA, we identified 16 significantly enriched hallmark gene sets, including 15 activated and one suppressed pathways (Supplementary Fig. 5B). Notably, the Complement (NES 2.11), PI3K-AKT-MTOR Signaling (NES 1.8), and Inflammatory response (NES 1.78) pathways were predominantly activated, while the MYC TARGETS V1 pathway (NES − 1.81) was suppressed (Fig. 7B). Our analysis revealed four significant pathways—Complement, IL2 STAT5 Signaling, Apoptosis, and Myc Targets V1—commonly implicated in sepsis and SLE. This intriguing overlap guided our subsequent investigation into shared DEGs within CD14⁺ monocytes across these two conditions. Employing a log₂FC cut-off of 0.25 to discern DEGs, we identified a shared repertoire of 34 upregulated and 27 downregulated genes (Fig. 7C). To gain deeper insights into the functional landscape of these shared DEGs, we conducted GO analysis on these genes. To reduce redundancy, all the GO terms, including BP, CC, and MF, were grouped into seven primary clusters. The results of the clustering analysis demonstrated that these DEGs were predominantly enriched in ribosome functionality and modulation by symbiont of host entry (Fig. 7D)

In this study, we identified 49 co-upregulated and 44 co-downregulated genes between sepsis and SLE datasets. Combined DEGs and WGCNA analysis, we identified 11 hub genes: ANKRD22, RSPH9, DHRS9, AIM2, CCNA1, CEACAM1, FBXO6, FCGR1A, FCGR1BP, TNFAIP6 and PLSCR1. In this investigation, we used SLE dataset as a training set and 11 hub genes as input to construct a LASSO regression model, showcasing its considerable diagnostic value for SLE. Furthermore, two feature genes within the model, TNFAIP6 and PLSCR1, significantly exhibited appreciable diagnostic utility within the sepsis dataset. To further explore the shared mechanism of immune dysregulation between sepsis and SLE, we conducted a single-cell analysis, and the results showed that the expression of the above vital genes was mainly increased in CD14⁺ monocytes. In addition, the CD14⁺ monocytes ratio was significantly higher in SLE and sepsis cases than in healthy individuals. Further analyses revealed shared pathway modifications between SLE and sepsis, such as the complement and MYC targets pathways. Collectively, our research findings suggest that both sepsis and SLE may share common cellular and molecular modifications.

Within the 11 identified hub genes, several genes have critical roles in immune system functions such as inflammation, cell-mediated cytotoxicity, and immune cell regulation. For instance, ANKRD22 serves a regulatory role in the inflammatory response, mediating the activation of macrophages following gastric mucosal injury [29]. The gene AIM2 has a role in dampening both constitutive expression and cytosolic DNA-induced expression of type I interferons, thereby participating in the pathogenesis of SLE via modulating inflammasome-dependent and independent mechanisms [30]. TNFAIP6, or TSG-6, is a secreted glycoprotein found in various cells and tissues. Its functions primarily centre on anti-inflammatory and tissue-protective activities [31]. Furthermore, PLSCR1, the most researched member of the scramblase protein family, interacts with numerous effectors and mediators, thus influencing various cellular processes [32]. A bioinformatics study by Zhong et al. disclosed PLSCR1 as one of the potential diagnostic genes for SLE in Chinese patients. Interestingly, the expression of PLSCR1 showed a positive correlation with the monocyte ratio [33].

As a part of our explorative process, we employed the 11 hub genes as inputs and developed LASSO regression models within the SLE dataset, resulting in a model that included TNFAIP6 and PLSCR1 as key features. Subsequent ROC trials in both the SLE test and validation sets yielded high AUC values. Additionally, when used individually, both TNFAIP6 and PLSCR1 displayed substantial diagnostic value in sepsis. These results suggest that TNFAIP6 and PLSCR1 hold promising diagnostic potential in sepsis and SLE. However, using these two critical genes as diagnostic markers for sepsis or SLE requires further clarification in large-sample clinical trials.

Our findings indicated an elevated expression of 11 hub genes in the peripheral blood of sepsis and SLE patients. However, the exact cell types that express these genes remain unclear. To address this issue, we embarked on a single-cell analysis, contrasting the sepsis and SLE patients with healthy individuals, respectively. This investigation revealed a significant increase in the proportion of CD14⁺ monocytes present in both sepsis and SLE. Moreover, upon examining cell-specific gene expression, CD14⁺ monocytes manifested higher hub gene module scores (based on UCell) and enhanced expression of most hub genes relative to other PBMCs. When we scrutinized gene expression patterns between disease and control groups, our data suggested that several hub genes were consistent with the results from bulk RNA analysis in sepsis and SLE patients. However, there were still differences in some other genes. This difference can be attributed to our single-cell data from PBMCs, which is crucial in the immune system but only represents a subset of all immune cells, excluding other types such as eosinophils and neutrophils. It's also critical to take into account a distinctive feature of scRNA-seq data, namely 'dropout' events, where a gene, albeit having moderate or low expression in one cell, goes undetected in another of the same type.

Considering the significantly elevated proportion and high module scores of CD14⁺ monocytes, our research next focused on this cell subset. We identified a series of genes within the IFN-inducible protein family, including IFI27, IFITM3, ISG15, and IFI6, which exhibited heightened expression in SLE patients compared to healthy controls. All these genes have been implicated in the interferon response, which is closely linked to SLE pathogenesis [34]. Notably, the gene PLSCR1 was significantly upregulated in the CD14⁺ monocytes of SLE patients, consistent with our above research results. In our examination of DEGs in CD14⁺ monocytes from sepsis patients, the S100 protein family, encompassing S100A8, S100A9, and S100A12, showed a significant upregulation, which plays important roles in the inflammatory response [35]. Moreover, one of the hub genes, FCGR1A, showed a significant upregulation in sepsis. The protein encoded by this gene, CD64, has been recognised as a potential diagnostic marker for the early diagnosis of neonatal infections [36].

To delve deeper into the common characteristics of CD14⁺ monocytes in sepsis and SLE, we conducted a GSEA. The results uncovered a significant overlap in the pathways of Complement, IL2 STAT5 Signaling, Apoptosis, and MYC Targets V1 in both disease states, suggesting a similar functional landscape for these cells across both conditions. Complement activation is an important part of immune response initiation in sepsis and a key pathogenesis of tissue inflammation and injury in SLE. Apart from its role in immunity, the complement system engages in numerous non-inflammatory processes and has a significant relationship with the coagulation system, which is also closely related to sepsis and SLE [37]. It is also worth noting the suppression of MYC Targets in CD14⁺ monocytes of sepsis and SLE patients. MYC is a recognized gene expression regulator involved in cell cycle progression, metabolism, apoptosis, differentiation and angiogenesis [38]. These findings prompt us to contemplate whether the dysfunction of CD14⁺ monocytes potentially contributes to the co-pathogenesis of sepsis and SLE, and that prolonged monocyte dysfunction in SLE patients may make them susceptible to sepsis. This hypothesis warrants corroboration through further experimentation.

Through an integrated analysis of bulk RNA and single-cell RNA sequence, our study illuminates potential links in the immune dysregulation of sepsis and SLE at both cellular and molecular levels, thereby broadening our understanding of these conditions. Despite these advancements, several limitations in our study merit acknowledgment. Firstly, our bioinformatic analysis depends on publicly accessible datasets, which may not encompass the complete range of SLE and sepsis patient profiles. Secondly, our findings primarily hinge on computational and statistical methodologies, necessitating further experimental validation for confirmation. Finally, while we pinpointed immune cell types associated with sepsis and SLE, a comprehensive grasp of the molecular mechanisms underpinning these associations and their implications for sepsis and SLE pathophysiology awaits elucidation.

In conclusion, we identified 11 hub genes, including ANKRD22, RSPH9, DHRS9, AIM2, CCNA1, CEACAM1, FBXO6, TNFAIP6, FCGR1A, PLSCR1, and FCGR1BP, that are significant for both sepsis and SLE. In addition, TNFAIP6 and PLSCR1 emerged as key genes with substantial diagnostic value for both conditions. Further, single-cell scrutiny revealed that these hub genes were primarily expressed in peripheral blood CD14⁺ monocytes, reconfirming that most hub gene expression levels were significantly elevated in the disease cohort relative to the control group. Both sepsis and SLE exhibited a marked upturn in monocyte proportions and shared similar pathway modifications, suggesting that CD14⁺ monocyte dysfunction is the common immune dysregulation of these two diseases.

SLE Systemic lupus erythematosus

GEO Gene Expression Omnibus

DEGs Differentially expressed genes

GO Gene Ontology

WGCNA Weighted gene co-expression network analysis

LASSO Least absolute shrinkage and selection operator

ROC Receiver operating characteristic

GSEA Gene Set Enrichment Analysis

Acknowledgements

Not applicable.

Author contributions

Xuehuan Wen, Songjie Bai and Kai Zhang designed the experiments, statistically analysed the data and wrote this manuscript. Shumin Li, Jie Yang, Qing Yu, Jiahui Li and Lanxin Cao collected and analysed the data. Gensheng Zhang and Zhijian Cai provided the conception, checked the results, provided valuable advice and reviewed the entire manuscript. All authors reviewed this manuscript.

Funding

This work was supported by the National Natural Science Foundations of China (No. 81971871, GS Zhang; No. 82100012, K Zhang) and Medical and Health Research Programs of Zhejiang Province (No. 2021KY174, GS Zhang; No. 2022498722, K Zhang).

Availability of data and materials

The datasets generated in this study are available in the GEO datasets (http://www.ncbi.nlm.nih.gov/geo/) and from the corresponding author on reasonable request.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflicts of Interest

The authors have no conflict of interest.

Caielli S, Wan Z, Pascual V: Systemic Lupus Erythematosus Pathogenesis: Interferon and Beyond. Annu Rev Immunol 2023, 41:533-60.
Tsokos GC: systemic lupus erythematosus. N Engl J Med 2011, 365(22):2110-21.
Barber MRW, Drenkard C, Falasinnu T, Hoi A, Mak A, Kow NY, Svenungsson E, Peterson J, Clarke AE, Ramsey-Goldman R: Global epidemiology of systemic lupus erythematosus. Nat Rev Rheumatol 2021, 17(9):515-32.
Crow MK: Pathogenesis of systemic lupus erythematosus: risks, mechanisms and therapeutic targets. Ann Rheum Dis 2023:ard-2022-223741.
Caielli S, Cardenas J, de Jesus AA, Baisch J, Walters L, Blanck JP, Balasubramanian P, Stagnar C, Ohouo M, Hong S et al: Erythroid mitochondrial retention triggers myeloid-dependent type I interferon in human SLE. Cell 2021, 184(17):4464-79.e19.
Tenbrock K, Rauen T: T cell dysregulation in SLE. Clin Immunol 2022, 239:109031.
Gordon C, Amissah-Arthur MB, Gayed M, Brown S, Bruce IN, D'Cruz D, Empson B, Griffiths B, Jayne D, Khamashta M et al: The British Society for Rheumatology guideline for the management of systemic lupus erythematosus in adults. Rheumatology (Oxford) 2018, 57(1):e1-e45.
Tektonidou MG LL, Hu J, Dasgupta A, Ward MM.: Survival in adults and children with systemic lupus erythematosus: a systematic review and Bayesian meta-analysis of studies from 1950 to 2016. Ann Rheum Dis 2017, 76(12):2009-16.
Dörner T, Furie R: Novel paradigms in systemic lupus erythematosus. The Lancet 2019, 393(10188):2344-58.
Barber MRW, Clarke AE: Systemic lupus erythematosus and risk of infection. Expert Rev Clin Immunol 2020, 16(5):527-38.
Fei Y, Shi X, Gan F, Li X, Zhang W, Li M, Hou Y, Zhang X, Zhao Y, Zeng X et al: Death causes and pathogens analysis of systemic lupus erythematosus during the past 26 years. Clin Rheumatol 2014, 33(1):57-63.
Oud L: Epidemiology and outcomes of sepsis among hospitalizations with systemic lupus erythematosus admitted to the ICU: a population-based cohort study. J Intensive Care 2020(8):3.
Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, Machado FR, McIntyre L, Ostermann M, Prescott HC et al: Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Medicine 2021, 47(11):1181-247.
Dugar S, Choudhary C, Duggal A: Sepsis and septic shock: Guideline-based management. Cleveland Clinic Journal of Medicine 2020, 87(1):53-64.
Sawada T, Fujimori D, Yamamoto Y: Systemic lupus erythematosus and immunodeficiency. Immunological Medicine 2019, 42(1):1-9.
Abid N, Khan AS, Otaibi FHA: Systemic lupus erythematosus (SLE) in the eastern region of Saudi Arabia. A comparative study. Lupus 2013, 22(14):1529-33.
Venet F, Schilling J, Cazalis MA, Demaret J, Poujol F, Girardot T, Rouget C, Pachot A, Lepape A, Friggeri A et al: Modulation of LILRB2 protein and mRNA expressions in septic shock patients and after ex vivo lipopolysaccharide stimulation. Hum Immunol 2017, 78(5-6):441-50.
Cazalis MA LA, Venet F, Frager F, Mougin B, Vallin H,: Early and dynamic changes in gene expression in septic shock patients: a genome-wide approach. Intensive Care Med Exp 2014, 2(1):20.
M F Carpintero LM, I Fernandez,A C Garza Romero,C Mejia: Diagnosis and risk stratification in patients with anti-RNP autoimmunity. Lupus 2015, 24(10):1057-66.
Chiche L, Jourde-Chiche N, Whalen E, Presnell S, Gersuk V, Dang K, Anguiano E, Quinn C, Burtey S, Berland Y et al: Modular transcriptional repertoire analyses of adults with systemic lupus erythematosus reveal distinct type I and type II interferon signatures. Arthritis Rheumatol 2014, 66(6):1583-95.
Nehar-Belaid D, Hong S, Marches R, Chen G, Bolisetty M, Baisch J, Walters L, Punaro M, Rossi RJ, Chung CH et al: Mapping systemic lupus erythematosus heterogeneity at the single-cell level. Nat Immunol 2020, 21(9):1094-106.
Reyes M, Filbin MR, Bhattacharyya RP, Billman K, Eisenhaure T, Hung DT, Levy BD, Baron RM, Blainey PC, Goldberg MB et al: An immune-cell signature of bacterial sepsis. Nat Med 2020, 26(3):333-40.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015, 43(7):e47.
Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L et al: clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb) 2021, 2(3):100141.
Friedman J HT, Tibshirani R.: Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 2010, 33(1):1-22.
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M: pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011, 12:77.
Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh PR, Raychaudhuri S: Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods 2019, 16(12):1289-96.
Andreatta M, Carmona SJ: UCell: Robust and scalable single-cell gene signature scoring. Comput Struct Biotechnol J 2021, 19:3796-98.
Liu J, Wu J, Wang R, Zhong D, Qiu Y, Wang H, Song Z, Zhu Y: ANKRD22 Drives Rapid Proliferation of Lgr5(+) Cells and Acts as a Promising Therapeutic Target in Gastric Mucosal Injury. Cell Mol Gastroenterol Hepatol 2021, 12(4):1433-55.
Choubey D, Panchanathan R: Absent in Melanoma 2 proteins in SLE. Clin Immunol 2017, 176:42-48.
Day AJ, Milner CM: TSG-6: A multifunctional protein with anti-inflammatory and tissue-protective properties. Matrix Biol 2019, 78-79:60-83.
Dal Col J, Lamberti MJ, Nigro A, Casolaro V, Fratta E, Steffan A, Montico B: Phospholipid scramblase 1: a protein with multiple functions via multiple molecular interactors. Cell Commun Signal 2022, 20(1):78.
Zhong Y, Zhang W, Hong X, Zeng Z, Chen Y, Liao S, Cai W, Xu Y, Wang G, Liu D et al: Screening Biomarkers for Systemic Lupus Erythematosus Based on Machine Learning and Exploring Their Expression Correlations With the Ratios of Various Immune Cells. Front Immunol 2022, 13:873787.
Luo S, Wang Y, Zhao M, Lu Q: The important roles of type I interferon and interferon-inducible genes in systemic lupus erythematosus. Int Immunopharmacol 2016, 40:542-49.
Xia C, Braunstein Z, Toomey AC, Zhong J, Rao X: S100 Proteins As an Important Regulator of Macrophage Inflammation. Front Immunol 2017, 8:1908.
Genel F, Atlihan F, Gulez N, Kazanci E, Vergin C, Terek DT, Yurdun OC: Evaluation of adhesion molecules CD64, CD11b and CD62L in neutrophils and monocytes of peripheral blood for early diagnosis of neonatal infection. World Journal of Pediatrics 2011, 8(1):72-75.
Oikonomopoulou K, Ricklin D, Ward PA, Lambris JD: Interactions between coagulation and complement--their role in inflammation. Semin Immunopathol 2012, 34(1):151-65.
Schulze A, Oshi M, Endo I, Takabe K: MYC Targets Scores Are Associated with Cancer Aggressiveness and Poor Survival in ER-Positive Primary and Metastatic Breast Cancer. Int J Mol Sci 2020, 21(21):8127.

sup1.tif
Supplementary Fig.1Selection of soft threshold for WGCNA. (A) The scale independence and mean connectivity of GSE95233. (B) The scale independence and mean connectivity of GSE61635.
sup2.tif
Supplementary Fig.2 Expression heatmap for hub genes in sepsis and SLE datasets. (A) Heatmap analysis of the hub gene expression comparing the sepsis group to healthy controls in GES95233. (B) Heatmap analysis of the hub gene expression comparing the SLE group to healthy controls in GSE61635.
sup3sle.tif
Supplementary Fig.3Clustering and Annotation Markers Characterization in SLE Single Cell Dataset GSE135779. (A) UMAP plots representing cell clusters of PBMCs from dataset GSE135779, each uniquely coloured to represent distinct clusters. (B) Annotation markers for the cell clusters. The X-axis denotes specific markers for each cell type, whereas the Y-axis corresponds to individual cell clusters.
sup4new.tif
Supplementary Fig.4 Cell Clusters, Annotation Markers, and Hub Genes Expression in the Sepsis Single Cell Dataset SCP548. (A) UMAP plots depict PBMC cell clusters from the dataset SCP548, each colour-coded uniquely to differentiate between clusters. (B) Annotation markers associated with each cell cluster. The X-axis presents the specific markers for each cell type, and the Y-axis corresponds to the individual cell clusters. (C) Heatmap of eleven hub genes' expression across all samples, the colour scale corresponds to the z-score.
sup5.tif
Supplementary Fig.5 GSEA Results for CD14⁺ Monocytes in SLE and Sepsis Compared to Controls. (A) GSEA outcomes for CD14⁺ monocytes in SLE. (B) GSEA outcomes for CD14⁺ monocytes in sepsis. The plot is divided into suppressed and activated sections. The X-axis represents the Normalized Enrichment Score (NES), and the Y-axis designates Hallmark gene sets.

Download PDF

Version 1

posted

You are reading this latest preprint version

Bulk and Single-Cell Transcriptome Analysis Reveal Shared Key Genes and Patterns of Immune Dysregulation in Sepsis and Systemic Lupus Erythematosus

Status:

Version 1

Abstract

Figures

Introduction

Methods

Data Collection and Processing

Identification of DEGs

Gene Ontology (GO) Analysis

WGCNA

LASSO Regression

ROC Curve

Analysis of Single-Cell RNA Sequence Data

Functional Enrichment Analysis of Key Genes

Statistical Analysis

Results

Common DEGs Identified in Sepsis and SLE and Their GO Analysis

Identify the module most positively associated with the diseases

Significant Diagnostic Value of TNFAIP6 and PLSCR1 in Sepsis and SLE

Increased Proportion and Hub Gene Overexpression in CD14⁺ Monocytes of SLE Patients

Elevated Proportion and Hub Gene Expression of CD14⁺ Monocytes in Sepsis

Parallel Pathway Alterations in CD14⁺ Monocytes of Sepsis and SLE

Discussion

Conclusion

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1

Bulk and Single-Cell Transcriptome Analysis Reveal Shared Key Genes and Patterns of Immune Dysregulation in Sepsis and Systemic Lupus Erythematosus

Status:

Version 1

Abstract

Figures

Introduction

Methods

Data Collection and Processing

Identification of DEGs

Gene Ontology (GO) Analysis

WGCNA

LASSO Regression

ROC Curve

Analysis of Single-Cell RNA Sequence Data

Functional Enrichment Analysis of Key Genes

Statistical Analysis

Results

Common DEGs Identified in Sepsis and SLE and Their GO Analysis

Identify the module most positively associated with the diseases

Significant Diagnostic Value of TNFAIP6 and PLSCR1 in Sepsis and SLE

Increased Proportion and Hub Gene Overexpression in CD14+ Monocytes of SLE Patients

Elevated Proportion and Hub Gene Expression of CD14+ Monocytes in Sepsis

Parallel Pathway Alterations in CD14+ Monocytes of Sepsis and SLE

Discussion

Conclusion

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1

Increased Proportion and Hub Gene Overexpression in CD14⁺ Monocytes of SLE Patients

Elevated Proportion and Hub Gene Expression of CD14⁺ Monocytes in Sepsis

Parallel Pathway Alterations in CD14⁺ Monocytes of Sepsis and SLE