Featured immune characteristics of COVID-19 and systemic lupus erythematosus revealed by multidimensional integrated analyses

Featured immune characteristics of COVID-19 and systemic lupus erythematosus revealed by multidimensional integrated analyses | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Featured immune characteristics of COVID-19 and systemic lupus erythematosus revealed by multidimensional integrated analyses Xingwang Zhao, Mengjie Zhang, Yuying Jia, Wenying Liu, Shifei Li, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2932364/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Sep, 2023 Read the published version in Inflammation Research → Version 1 posted 7 You are reading this latest preprint version Abstract Coronavirus disease 2019 (COVID-19) shares similar immune characteristics with autoimmune diseases like systemic lupus erythematosus (SLE). However, such associations have not yet been investigated at the single-cell level. Thus, in this study, we integrated and analyzed RNA sequencing results from different patients and normal controls from the GEO database and identified subsets of immune cells that might involve in the pathogenesis of SLE and COVID-19. We also disentangled the characteristic alterations in cell and molecular subset proportions as well as gene expression patterns in SLE patients compared with COVID-19 patients. Key immune characteristic genes (such as CXCL10 and RACK1) and multiple immune-related pathways (such as the coronavirus disease-COVID-19, T-cell receptor signaling, and MIF-related signaling pathways) were identified. We also highlighted the differences in peripheral blood mononuclear cells (PBMCs) between SLE and COVID-19 patients. Moreover, we provided an opportunity to comprehensively probe underlying B-cell‒cell communication with multiple ligand‒receptor pairs (MIF-CD74 + CXCR4, MIF-CD74 + CD44) and the differentiation trajectory of B-cell clusters that is deemed to promote cell state transitions in COVID-19 and SLE. Our results demonstrate the immune response differences and immune characteristic similarities, such as the cytokine storm, between COVID-19 and SLE, which might pivotally function in the pathogenesis of the two diseases and provide potential intervention targets for both diseases. COVID-19 Systemic lupus erythematosus Single cell RNA sequence Immune cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Coronavirus disease 2019 (COVID-19) is an infectious disease, which is attribute to infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has recently spread rapidly worldwide and led to great human hardship. Systemic lupus erythematosus (SLE) is a chronic relapsing multisystem autoimmune disease of unknown etiology, and patients with SLE may have a high risk of acquiring SARS-CoV-2 infection [ 1 ]. The coronavirus shares a common host cell entry receptor, angiotensin converting enzyme 2 (ACE2), of which the aberrant upregulation has been observed in T cells from SLE patients [ 2 ]. Patients with SLE are susceptible to SARS-CoV-2 infection due to innate immune disorders and increased inflammation [ 3 ]. Once infected with coronavirus, SLE patients exhibit severely weakened and altered immune systems [ 4 , 5 ]. Moreover, SARS-CoV-2 infections can induce other autoimmune diseases including autoimmune kidney disease as well as some rheumatic complications [ 4 , 6 – 8 ]. Several studies have reported that coronaviruses may be correlated with the pathogenesis of autoimmune diseases. Some specific inflammatory and immune responses are involved in the pathogenesis of both COVID-19 and autoimmune disease [ 5 ]. Several similarities between COVID-19 and autoimmune diseases such as SLE mainly include aberrant immune responses such as proinflammatory cytokines, lymphopenia, and aberrant T- and B-cell responses [ 9 ]. To some extent, organ injury in COVID-19 patients correlates with host immune status, as observed in patients with SLE [ 10 ]. In addition, SLE and COVID-19 share some of the same complications including leukopenia, lymphocytopenia, thrombocytopenia, and vasculitis [ 11 ]. Some autoantibodies associated with SLE can also be detected in SARS-CoV-2-infected patients [ 7 ]. Furthermore, the microbiome characteristics of COVID-19 patients are similar to those of SLE patients [ 12 ]. All these findings suggest that crosstalk exists between implicated inflammatory pathways/mechanisms and clinical characteristics in both SLE and COVID-19 patients. However, there are few reports on the common molecular mechanisms of COVID-19 and SLE [ 13 ]. The mechanisms underlying SARS-CoV-2 infection modulates the risk of autoimmune disease including SLE has not been fully elucidated [ 14 ]. Human leukocyte antigens (HLA) molecules have been shown to play an important role in protective immunity as well as in disease-causing autoimmune responses [ 15 ]. New evidences have shown that the HLA gene sets were associated with SLE [ 16 ]. The HLA gene sets, as part of host genetic factors, contribute to disease susceptibility and prognosis. HLA is crucial for appropriate immune responses in SARS CoV-2 infections [ 17 ]. A recent study provides evidence that HLA genotype influences clinical outcome in COVID-19 [ 18 ]. We thus investigated the association of HLA gene sets with SLE and COVID-19 in this study. Moreover, the efficiency and safety of COVID-19 vaccines for the prevention of COVID-19 in SLE patients is unclear, especially in the patients receiving immunosuppressive treatment. In many cases, the current understanding of peripheral blood immune cells in SLE patients infected with COVID-19, which may be modulated during disease, is limited to flow cytometry assay, but the specific status of immune cells such as T and B cells, remains unclear. Single-cell transcriptome analysis facilitates discrimination of the disease specific immune status. Single-cell RNA sequencing (scRNA-seq) methods through the unbiased clustering of cell populations can obviously capture potential pathogenic drivers and biological targets [ 19 ], which have been utilized to discriminate specific inflammatory cell states and identify specific immune cell populations in healthy subjects or diseased patients such as those with SLE [ 20 , 21 ]. Therefore, investigating the potential associations and molecular mechanisms between COVID-19 and SLE by analyzing high-throughput sequencing data is particularly urgent to help in diagnosing and treating both diseases. In this study, the distinct immune subtypes and molecular subtype pattern characteristics of COVID-19 and SLE cohorts were recognized. We also explored the transcriptional features of human peripheral blood mononuclear cells (PBMCs) from COVID-19 and SLE patients at the single-cell level to reveal the molecular mechanisms of specific disease processes. We then analyzed the single-cell immunological landscape to reveal immune cell types that might contribute to immunotherapy development. The key immune-related gene regulators in our results may serve as good biomarkers for diagnosing and monitoring the efficacy of immunotherapy. The common molecular pathway characteristics of the two diseases revealed in our study may also help scientists develop specific COVID-19 therapies and vaccines. 2. Materials and Methods 2.1 Datasets acquisition and preprocessing Normalized microarray datasets were acquired from the Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo/ ). Six transcriptomic RNA sequencing datasets, namely, GSE157103 (whole blood, COVID-19), GSE161731 (whole blood, COVID-19), GSE163151 (whole blood, COVID-19), GSE72509 (whole blood, SLE), GSE49454 (whole blood, SLE) and GSE110169 (whole blood, SLE), and six single-cell transcriptomic RNA sequencing datasets, namely, GSE135779 (PBMCs, SLE), GSE142016 (PBMCs, SLE), GSE155222 (PBMCs, COVID-19), GSE166992 (PBMCs, COVID-19), GSE163121 (B cells, SLE), and GSE164379 (B cells, COVID-19), were obtained. The relevant information of the datasets in this study is displayed in Supplementary Table 1 (Table S1 ) . The clinical characteristics of the COVID-19 patients, SLE patients and healthy controls are shown in Tables S2 and S3 . 2.2 Verification of immunity-related subtypes and differentially expressed genes By using single-sample gene set enrichment (ssGSEA) of “ Gene set variation analysis (GSVA) ” R package to quantify the enrichment levels of the 29 immune-associated maker gene sets in each sample, which covered diverse immune cell types, functions, and pathways, and performed hierarchical clustering of samples, we evaluated the immune infiltration landscape of SLE and COVID-19 patients from datasets and the immune status of the subjects was divided into two immunophenotype groups (low immunity (immune_L) and high immunity (immune_H)) [ 22 , 23 ]. The immune-related differentially expressed genes (DEGs) were determined by expression comparison between the two immunophenotype groups using the limma package of R software (adjusted P 1) [ 24 ]. The gene sets with immune-relation were downloaded from the Immunology Database and Analysis Portal (ImmPort) database ( http://www.immport.org ) [ 25 ]. Furthermore, the relationship between the immune microenvironment and HLA gene expression in the two groups was analyzed. 2.3 WGCNA Weighted gene coexpression network analysis (WGCNA) is an R package for weighted correlation network analysis [ 26 ]. We conducted hierarchical clustering of samples to cluster modules. The dynamic cutting algorithm was used to validate the gene modules. Subsequently, the characteristic genes were clustered and merged into modules. The gene network can be divided into different modules according to the similarity of expression, the leftmost color block represents the different gene modules and the rightmost color bar represents the correlation range. In the middle part of the heat map, the darker the color, the higher the correlation. Red indicates positive correlation, and green indicates negative correlation. The numbers in each cell indicate relevance and significance. Module-trait relationships between each module and the clinical characteristics were identified based on Pearson correlation. 2.4 Functional annotation with molecular subtypes Three COVID-19 and SLE cohorts were used to develop the molecular subtype classification. Principal component analysis (PCA) was used for data dimensionality reduction and visualization. 2.5 Gene set variation analysis (GSVA) We used the GSVA R package to study the activity of biological pathways among molecular subtypes [ 23 ]. The ‘c2.cp.kegg.v7.0.symbols’ gene sets were obtained from Release 6.0 of the Molecular Signatures Database (MSigDB) for GSVA analyses [ 27 ]. The clusterProfiler R package was used to conduct Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses between distinct subtypes [ 28 ]. 2.6 Cell clustering annotation and visualization The Seurat v4.0.0 package of R software (v4.0.3) was used to perform the unsupervised clustering [ 29 ]. The data were normalized to transcript copies per 10,000 and log-normalized to reduce sequencing depth variability. After quality control and filtering, the variation coefficient of genes was analyzed with Seurat, and 2000 genes were chosen for further analysis. Uniform Manifold Approximation and Projection (UMAP) plots was adopted for cluster identification and dimensionality reduction of visualization. The FindConservedMarkers was adopted for identification of the clusters. The parameters setting criteria are logFC.threshold > 0.25, minPct > 0.25 and Padj ≤ 0.05. Then, clusters were annotated based on the DEGs and the canonical cellular markers according to the Cell Marker database [ 30 ]. 2.7 Gene set enrichment analysis (GSEA) and Single-sample GSEA (ssGSEA) The immune status in COVID-19 and SLE was evaluated by ssGSEA. The enrichment of a gene set was indicated by the enrichment fraction in each sample. The pathway enrichment analysis was performed using the GSEA in patients with different subtypes of COVID-19 or SLE according to the MSigDB. GO and KEGG pathway analyses were also conducted with GSEA and a false discovery rate (FDR) ≤ 0.05 was defined as obviously enriched [ 31 ]. The circus plots were drawn by using R v4.0.3. 2.8 Intercellular communication analysis To study potential intercellular communication between the B-cell subsets, we used Cell Chat package in R to evaluate ligand-receptor expression and distribution of B-cell subsets through standard pipelines as described previously [ 32 ]. We used Cell Phone DB (ligand-receptor pair list, www.cellphonedb.org ) [ 33 ] to identify cell‒cell interactions between B-cell subsets. 2.9 Trajectory analysis The cellular dynamic processes of B-cell subpopulation differentiation [ 34 ] and the origination were modeled using a trajectory inference method. Specifically, the differential GeneTest [ 35 ] was used to calculate the top 150 signature genes. Monocle default parameters were used to infer B-cell differentiation trajectories after dimensionality reduction and cell ordering. The ‘DDRTree’ and ‘plot_cell_trajectory’ were utilized for dimensionality reduction and for visualization, respectively. 2.10 Statistical analysis The software R v4.0.3 and corresponding R packages ( http://www.bioconductor.org/ ) were used to perform statistical analysis. Spearman’s correlation was utilized to analyze correlations. The comparisons between two groups were examined by means of Wilcoxon test. All statistical P values ( P < 0.05) represents statistical significance. 3. Results 3.1 Immune status and immune-related DEGs in COVID-19 and SLE patients We searched six gene expression datasets representing samples from COVID-19 and SLE patients and samples from normal controls to start the analyses. We analyzed the immune status using the ssGSEA method, and performed clustering by dividing the COVID-19 patients into two groups, the immune_H group and immune_L group. Analysis of the difference in the distributions between both groups indicated that the stromal scores of the immune_H group were higher ( P < 0.01) based on the Mann-Whitney U test, consistent with the estimate scores and immune scores, as shown in Fig. 1 A and 1 B. We also analyzed the relationship between both immune types and HLA gene sets. The results demonstrated that the samples in the immune_H group showed 12 genes (HLA-DMA, HLA-DMB, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DPB2, HLA-DQA1, HLA-DQA2, HLA-DQB2, HLA-DRA, HLA-DRB6, HLA-L) with higher expression and 6 genes (HLA-A, HLA-C, HLA-E, HLA-B, HLA-F and HLA-J) with significantly lower expression than those in the immune_L group (Fig. 1 C). Furthermore, the immune_L group had more neutrophils while the immune_H group had more resting NK cells and CD8 + T cells (Fig. 1 D). Ninety-eight immune-related DEGs were found between the both groups ( Supplementary Figure S1 (Figure S1 ) and Fig. 1 E). In addition, GSEA analysis of the immune-related genes showed that COVID-19 patients were positively associated with allograft rejection biological processes (Fig. 1 F). WGCNA was used to describe the associations between the module eigengenes and clinical characteristics. The analysis confirmed that the pink module had significant positive correlation with hospital-free days and that the blue module had negative correlation with mechanical ventilation (Fig. 1 G). We also divided the SLE patients into immune-high and -low groups by hierarchical clustering. That is the immune_H group has higher immune scores and vice versa (Figs. 2 A and 2 B). The relationship between both immune types and HLA gene sets demonstrated that the samples in the immune_H group had significantly higher expression of 12 genes (HLA-DMA, HLA-DOA, HLA-DMB, HLA-DPB1, HLA-DPA1, HLA-DPB2, HLA-DQA2, HLA-DQB2, HLA-DQA1, HLA-DRB5, HLA-DRA, HLA-DRB6) and 6 significantly lower expression of genes (HLA-A, HLA-C, HLA-E, HLA-B, HLA-L and HLA-F) compared with the immune_L group (Fig. 2 C). Furthermore, the immune_L group had more plasma cells and naive CD4 + T cells, while the immune_H group had more regulatory T cells (Treg), memory B cells, CD8 + T cells, neutrophils and resting NK cells (Fig. 2 D). Total 112 immune-related DEGs between both groups were obtained (Fig. 2 E and Figure S1 ). WGCNA revealed that the associations between the module eigengenes and clinical characteristics were not significant (Fig. 2 F). In addition, GSEA analysis of the immune-associated genes showed that SLE was negatively correlated with propanoate metabolism, and valine leucine and isoleucine degradation (Fig. 2 G). There are several studies demonstrated that the possible etiological mechanisms of SLE and COVID-19 include immunometabolism dysfunction [ 36 – 40 ]. Our results provide evidence that metabolites may be involved in the development of SLE, which might provide novel insights into investigating causal role of blood metabolites in development of SLE through a comprehensive genetic pathway. To identify immune-related DEGs that contribute to SLE and COVID-19 pathogenesis, we analyzed the six cohorts to identify immune-related DEGs with significant differences (P 2, P < 0.01) that were significantly related to SLE and COVID-19 (Fig. 2 H), among which C-X-C motif chemokine ligand 10 (CXCL10) showed a higher level of expression in patients of both groups. The CXCL10 was increased in various autoimmune diseases like SLE [ 41 ], which may also be a key regulator of the 'cytokine storm' immune response to SARS-CoV-2 infection [ 42 ]. The common immune-related DEGs (CD3-TCR complex (CD3D), CXCL10, SH2 domain-containing 1A (SH2D1A), SH2 domain-containing 1B (SH2D1B)) of the six cohorts were obtained by the expression comparison of the DEGs in both groups with different immune status (Fig. 2 H and Figure S2 ). All of these genes were upregulated in patients with COVID-19 and SLE ( Table S4 ). 3.2 Immune-related molecular subtype and pathway characteristics of SLE and COVID-19 patients We further analyzed the immune-related molecular subtype and pathway characteristics of the SLE and COVID-19 patients. The GSE157103, GSE161731 and GSE163151 cohorts were enrolled into one COVID-19 cohort. Based on the gene expression status, three subtypes were obtained by unsupervised clustering (Fig. 3 A). The proportion of males was a clear different between subgroups I and II (Fig. 3 B). The proportion of age was also significantly distinction between subgroups I and II (Fig. 3 C). Subsequently, the relationship heatmap between eigengenes and clinical characteristics (Fig. 3 D) and the module eigengene dendrogram (Fig. 3 E) were obtained. Moreover, Gene Ontology (GO) and KEGG analyses were conducted to indicate the biological behavior among the three subgroups ( Figure S3 and Fig. 3 F). The different pathways associated with COVID-19 included the spliceosome, ferroptosis, tryptophan metabolism, T-cell receptor signaling, coronavirus disease-COVID-19, ribosome, malaria, EGFR tyrosine kinase inhibitor resistance, insulin signaling and cell cycle (Fig. 3 F). The GSE72509, GSE49454 and GSE110169 cohorts were enrolled into one SLE cohort for comparison with the COVID-19 cohorts (Fig. 3 G). There was no difference in the proportion of females between subgroups I and II (Fig. 3 H). The associations between eigengenes and clinical characteristics are shown in Fig. 3 I. The GO and KEGG pathways associated with SLE included coronavirus disease-COVID-19, T-cell receptor signaling, ribosome, graft-versus-host disease, ECM-receptor interaction, nitrogen metabolism and spliceosome ( Figure S4 and Fig. 3 J). Therefore, the different pathways associated with both SLE and COVID-19 included coronavirus disease-COVID-19, T-cell receptor signaling, ribosome, graft-versus-host disease, extracellular matrix (ECM)-receptor interaction and spliceosome. 3.3 Comparison of the single-cell immune transcription atlas of PBMCs between SLE and COVID-19 patients To explore the transcriptional features of PBMCs from SLE and COVID-19 patients at the single-cell level, we characterized the cell and molecular mechanisms of specific disease processes. ScRNA-seq was used to identify specific immune cell subsets of COVID-19 and SLE patients. After stringent quality control and filtering by multiple criteria. Transcriptomes of 25,059 and 23,902 single cells from COVID-19 and SLE samples belonging to four single-cell datasets, namely, GSE135779 (PBMCs, SLE), GSE142016 (PBMCs, SLE), GSE155222 (PBMCs, COVID-19) and GSE166992 (PBMCs, COVID-19), were obtained, with about 17,474 and 16,559 genes per cell detected, respectively. Then, the single-cell datasets were combined for further systematic comparison between SLE and COVID-19 samples. Based on PCA, the similar expression patterns of cells were cluster by using the UMAP algorithm. The analysis results further classified the two group samples into 21 clusters (Fig. 4 A, B). We then performed a comparative study of the composition of PBMC subpopulations between the SLE and COVID-19 samples, we found that some cell clusters (including cluster 2, unknown 1 cells; cluster 11, unknown 2 cells; cluster 17, unknown 4 cells; cluster 14, platelet cells) were decreased in SLE patients (Fig. 4 B) and that some cell subpopulations (cluster 0, CD14 + mono1 cells; cluster 9, CD8 + naive T cells; cluster 8, CD4 + memory T cells; cluster 3, CD8 + effector T cells; cluster 6, CD8 + T cells; cluster 10, CD14 + monocyte 2 cells) were increased in SLE samples (Fig. 4 B ) . Considering the high dropout rate of scRNA-seq data, we here selected the dropClust, which is a novel algorithm for clustering and visualization of ultra-large single cell RNA-seq (scRNA-seq) data [ 43 ]. We classified main cells sub-populations based on various cell markers. Finally, 16 cell clusters were identified, and other 5 cell clusters were “unknown” cell clusters (Fig. 4 C-F). The cell type-specific gene expressions were shown in the dot plot ( Fig. 4 C ) and the UMAP plot for cell clustering visualization (Fig. 4 D). No specified unique cell subset in either group was detected based on the expression level of gene markers (refer to the database of CellMarker) (Fig. 4 E). The top 3 DEGs of each subset are listed in Fig. 4 F. CD14 + mono1 cells, naive T cells, unknown 1 cells, CD8 + effector T cells and CD4 + T cells were the most abundant cell types (Fig. 4 G). For the SLE patients, the dominant cell clusters were CD14 + monocytes, naive T cells, CD8 + T cells, CD8 + effector T cells, CD4 + T cells, and CD4 + memory T cells. However, in COVID-19 patients, unknown 1 cells, naive T cells, naive B1 cells, CD4 + T cells, unknown 2 cells, and naive CD4 + T cells were the top 6 dominant cell clusters. The transcriptomic features of each cluster were displayed based on gene expression levels across all PBMCs in the COVID-19 and SLE samples (Fig. 5 A). The cell populations of the SLE and COVID-19 samples were also enriched in expressing CD3D, granulysin (GNLY) and CD79a molecule (CD79A) in the dot plot (Fig. 5 B). As the unifying marker in COVID-19 patients, the ATP synthase (ATP5E) gene was downregulated while receptor for activated protein C kinase 1 (RACK1) gene was upregulated compared with SLE patients ( Fig. 5 C-D ) . We also found several differentially expressed genes, including RACK1 and ATP synthase F1 subunit epsilon (ATP5F1E), in SLE patients compared with COVID-19 patients from various cell clusters (Figs. 5 E-J and Figure S5 ). The results suggested a significant difference in the B-cell region in the two diseases. Therefore, we selected the B-cell datasets of the two diseases to further explore the underlying differences in characteristics. 3.4 Differential immune characteristics of B-cell subpopulations in SLE and COVID-19 patients To compare the immune characteristics of B-cell subpopulations between SLE and COVID-19 samples, the recently published B-cell single-cell datasets (GSE163121 (B cells, SLE), GSE164379 (B cells, COVID-19)) were merged for further systematic comparison between SLE and COVID-19 samples. Transcriptomes of 14,685 and 13060 single B cells from the COVID-19 and SLE patients were obtained, with 15,763 and 14,590 genes per cell detected, respectively. The analysis results classified the two group samples into 15 clusters using the UMAP plot (Fig. 6 A). Compared to COVID-19, the results showed that immune-related B-cell clusters, such as cluster 4, MT + B cells; cluster 6, CD83 + B cells; and cluster 12, STAT4 + B cells, were decreased (Fig. 6 A, B). These B-cell subpopulations (cluster 1, CXCR5 + B cells; cluster 5, HLA-DRB5 + B cells) in the SLE samples were increased (Fig. 6 A, B ). Among the B cell subpopulations, TCL1A + naive B1 cells, CXCR5 + B cells, CD80 + B cells, TCL1A + naive B2 cells, MT + B cells, HLA-DRB5 + B cells, CD83 + B cells and CCL5 + B cells were the most abundant B-cell types (Fig. 6 B ) . For the SLE patients, the dominant cell clusters were CXCR5 + B and HLA-DRB5 + B. However, in the COVID-19 patients, MT + B cells, CD83 + B cells, CCL5 + B cells, and STAT4 + B cells were the top 4 dominant cell clusters. Based on the expression of main gene markers (refer to the Database of CellMarker), we identified 15 cell subsets, as shown in Fig. 6 C-E. The dot plot shows the expression levels of marker genes of well-known cell type ( Fig. 6 C ) . There was no specified unique cell subset in either group was detected based on the expression level of gene markers (Fig. 6 D), the top 3 DEGs of each subset are listed in Fig. 6 E. The cell populations of the SLE and COVID-19 samples were both enriched in the expression of the marker genes CD79A, IL7R and membrane spanning 4-domains A1(MS4A1) (Fig. 7 A). As a unifying marker in SLE patients, there was higher expression of AC090498.1 and lower expression of RACK1 compared to COVID-19 patients ( Fig. 7 B, C ) . We found several differentially expressed genes, including RACK1, ATP5F1E, ATP5E and AC090498.1, in the SLE samples compared with the COVID-19 samples from various B-cell clusters (Figs. 7 D-M). To investigate the role of B cells in SLE and COVID-19, the pseudotime methods were used to simulate the differentiation trajectory of B cells. We divided the samples into multiple cell populations (states) under differentiation states according to the gene expression status, and generate an intuitive lineage development tree diagram to predict the differentiation and development track of cells. A total of 11 cell types and 12 states were identified in the SLE samples ( Fig. 8 A ) , and 15 cell types and 5 states were subsequently identified in the COVID-19 samples ( Fig. 8 B ) . Here, genes that were markedly differentially expressed along the pseudotime axis were further clustered into 11 modules according to their expression patterns in the SLE samples in a heatmap (Fig. 8 C ) . Previous findings suggested that cluster 2 was mainly composed of CD83 + B cells, we thus used the branch of state 9 as the starting point ( Fig. 8 D ) . For COVID-19, the genes that were markedly differentially expressed along the pseudotime axis were further clustered into 15 modules according to the expression patterns (Fig. 8 E). State 5 contained more B cells from the COVID-19 group compared with the SLE group ( Fig. 8 F ) . Previous findings indicated that cluster 5 was primarily composed of S100A10 + B cells, we that used the branch of state 5 as the starting point ( Fig. 8 F ) . Then, along the pseudotime axis, the expression of encoding genes was examined in the SLE and COVID-19 groups ( Fig. 8 G, H ) . In our study, the comparisons of B cells by single-cell RNAseq provided evidence that B cells in diverse infectious diseases and in systemic autoimmune diseases are highly related and conducive to exploration share common drivers of differentiation and expansion. The above results suggested that the B cells expanded in different directions in the SLE and COVID-19 groups and that the B-cell clusters may drive different heterogeneity and cell state transitions in COVID-19 and SLE. To investigate this hypothesis, we applied CellChat functionalities to B-cell scRNA-seq datasets of COVID-19 and SLE. CellChat identifies communication patterns and predicts functions [ 32 ]. Our results showed that B-cell clusters may communicate with other B-cell clusters via the MIF signaling pathway, thereby regulating cell function (Fig. 9 A-B). Moreover, the MIF signaling pathway networks (Fig. 9 C-D) showed not only high signaling redundancy but also high target promiscuity (for instance, B-cell clusters can act as MIF targets). Cells have unique communication modes, including senders, mediators, receivers and influencers. Complex signaling networks were dissected by explicitly assigning sender and receiver cells to distinguish the MIF signaling pathway network. It demonstrated that the MIF signaling pathway network in the COVID-19 and SLE patients is of high redundancy, and multiple ligand sources can target most of B-cell clusters (Fig. 9 E-F). Further analysis of ligand‒receptor demonstrated that immune signaling axes (MIF-CD74 + CXCR4 and MIF-CD74 + CD44) might participate in the intercellular crosstalk between B-cell subsets (Fig. 9 G-H). Together, these results provide an opportunity to deeply probe underlying B-cell‒cell communication with multiple ligand‒receptor pairs (MIF-CD74 + CXCR4, MIF-CD74 + CD44) that are often deemed to drive heterogeneity and cell state transitions in COVID-19 and SLE. 4. Discussion The COVID-19 pandemic has become a major concern worldwide [ 7 ]. Patients with SLE, an autoimmune disease, are susceptible to COVID-19, which has attracted much attention in the context of the current pandemic. There are many similarities and differences between COVID-19 and SLE. SLE patients are at high risk of becoming infected with the coronavirus. Further examination of the molecular mechanisms in COVID-19 and SLE patients may contribute to finding better therapeutic options. Therefore, we systematically conducted a comparative analysis of SLE and COVID-19 from multiple perspectives. First, we focused on the molecular characterization of the two diseases as it relates to the immune system, and we identified the common upregulated immune-related genes CD3D, CXCL10, SH2 domain containing 1B (SH2D1B)) by comparing two immunity clusters. Among the above immune-related gene regulators, CXCL10 is a proinflammatory chemokine that is involved in COVID-19 that leads to acute respiratory distress syndrome (ARDS) [ 44 ]. CXCL10 could be a key candidate gene related to the cytokine storm of COVID-19 ARDS patients [ 45 ]. One study has shown that CXCL10 is upregulated in blood samples of COVID-19 patients [ 46 ]. It is expected to become a therapeutic target [ 42 ]. A previous study also demonstrated that CXCL10 is upregulated in PBMCs and B lymphocytes, serum and/or tissue of patients with SLE [ 41 , 47 ]. Moreover, CXCL10 responds to IFN-γ pathway inhibition [ 48 ]. Therefore, we speculated that targeting CXCL10 may be a promising strategy for treating SLE patients who also have COVID-19. COVID-19 vaccines are urgently needed to control the pandemic, for patients with SLE, scientific vaccination helps to reduce the risk of infection in order to achieve an adequate and durable immune response [ 49 ]. The association study about the association of HLA gene sets with immune status between SLE and COVID-19 could help screen potential targets for the immune system and explore the impact of the vaccine on protective immunity. Individual HLA alleles can influence the risk and the severity of viral infections. For COVID-19, these analyses may contribute to assessing the susceptibility of SARS-CoV-2 and the epidemiological level [ 50 ]. Clarification of these specific differences in HLA subtype between different groups may contribute to predicting the differential susceptibility or resistance to SARS-CoV-2 infections, especially for SLE patients. Considering that gene regulation is a complicated multisystem adjustment process, we also studied the integrated role of genes in the molecular subtypes of COVID-19 and SLE. Our study demonstrated that the signaling pathways of coronavirus disease-COVID-19, T-cell receptor signaling, ribosome, graft-versus-host disease, ECM-receptor interaction, and spliceosome were significantly activated in COVID-19 and SLE molecular subtypes. Significantly distinct pathway characteristics were observed in the different molecular subtypes, which also confirmed that the specific regulation pathways were remarkably associated with COVID-19 and SLE. Our results may contribute to facilitating the study of the relationships between molecular subtypes and especially pathways among COVID-19 and SLE. Previous studies have revealed that SLE with high heterogeneity and complexity features [ 51 ] is very challenging regarding both precision diagnosis and treatment when SLE patients also have COVID-19. According to these findings, the examination of immune cell dysfunction in COVID-19 and SLE patients may help to discover new pathogenesis mechanisms. It is also important for the clinical management of SLE and COVID-19 patients. Therefore, it is urgent to reveal the underlying molecular mechanism by high cellular resolution strategy in SLE and COVID-19 patients. Herein, scRNA-seq methods were used to identify the molecular signature and cellular features to dissect their role in these two diseases at the single-cell level. There are 21 and 15 predominant subpopulations of cells with UMAP clustering were identified in the PBMCs and B cells of COVID-19 and SLE patients, respectively. Notably, the features of the cells with distinct transcriptomic patterns were recognized. For the PBMCs of SLE patients, the dominant cell clusters were CD14 + monocytes, CD8 + effector T cells, CD8 + T cells, naive T cells, CD4 + T cells, and CD4 + memory T cells. However, for COVID-19 patients, unknown 1 cells, naive T cells, naive B1 cells, CD4 + T cells, unknown 2 cells, and naive CD4 + T cells were the top 6 dominant cell clusters. Moreover, the results suggested that the significant difference in B cells restored the pathogenesis of the two diseases. Thus, we selected the B-cell datasets of the two diseases to further explore the underlying differences in characteristics. Based on comparative analysis, for the SLE patients, the dominant cell clusters were CXCR5 + B and HLA-DRB5 + B cells. However, in the COVID-19 patients, MT + B cells, CD83 + B cells, CCL5 + B cells, and STAT4 + B cells were the top 4 dominant cell clusters. In general, our results demonstrated that differences in the B-cell subpopulations existed between the two groups. The differences in the B-cell subpopulations existed between the two groups showed that B-cell clusters might drive cell state heterogeneity and transitions in COVID-19 and SLE. B-cell clusters in diverse infectious diseases and in systemic autoimmune diseases are highly related, which share respective drivers of differentiation and expansion. However, B-cell clusters in different diseases are not identical and even show discrete disease-specific features. As we know, B cells can recognize the antigen (foreign body) and produce antibodies against it. B cells involves in humoral-mediated immunity or antibody-mediated immunity (AMI). They fight and protect the body from the virus that enters the bloodstream [ 52 ]. A better understanding of the commonality and differences in the B cell responses in these two diseases may provide critical insights into the development of vaccines that drive pathogen-specific antibody responses and avoid autoimmunity. It is well established that B cells are one of the main immune cells with abnormal differentiation in SLE patients [ 53 ]. Analyzing the transcription atlas of relevant B-cell subsets in SLE patients would facilitate new ideals to targeting pathogenic B cells. In our study, pseudotime trajectory analyses indicated that B cells may drive heterogeneity and cell state transitions in COVID-19 and SLE. A total of 11 cell types and 12 states were identified in the SLE group, and a total of 15 cell types and 5 states were subsequently identified in the COVID-19 group. Based on our findings that cluster 2 primarily consisted of CD83 + B cells, the branch of state 9 was the starting point. State 5 comprised more S100A10 + B cells from the COVID-19 group compared with the SLE group; thus, the branch of state 5 was set as the starting point. These results suggested that B cells in the SLE and COVID-19 groups were expanded in different directions. Moreover, different intimate cell‒cell communications among B-cell clusters were identified in the two diseases. We also found that the high expression of receptor‒ligand complexes, such as MIF-CD74 + CXCR4 and MIF-CD74 + CD44, may play crucial roles in SLE and COVID-19 patients. The activation of MIF-related pathways is participated in the formation of B-cell communication in the two diseases. Our findings provide novel insight to support that MIF-related signaling pathways might potentially serve as an underlying molecular mechanism of B cells in SLE and COVID-19. Thus, this finding suggests that the B-cell populations could be the candidate targets for MIF inhibitors. In addition, we also discovered several unidentified differentially expressed genes, such as RACK1, ATP5F1E and AC090498.1, in the SLE samples compared to the COVID-19 samples from various fine-sorted B-cell clusters. The differential expression of the key genes we identified in PBMCs and B cells might facilitate our understanding of SLE and COVID-19. Through database analysis, our results showed that these genes that are related to immune cells and involved in the regulation of immune cell activity. For instances, RACK1 was identified as a novel host factor required for Zika virus (ZIKV) replication. The experiments of depletion of RACK1 demonstrated that RACK1 is important for replication of SARS-CoV-2 [ 54 ]. A better understanding of the commonality and differences about these key genes in the PBMC and B cell in these two diseases might provide critical insights into the development of vaccines that drive pathogen-specific antibody responses and avoid autoimmunity. A recent study reported that Omicron RBD memory B cell recognition was substantially reduced to 42% compared with other variants in subjects 6 months post-vaccination [ 55 ]. Understanding the SARS-CoV-2 RBD-specific naive repertoire may inform potential responses capable of recognizing future SARS-CoV-2 variants or emerging coronaviruses, enabling the development of pan-coronavirus vaccines aimed at engaging protective germline response [ 56 ]. Memory B cell reserves can generate protective antibodies against repeated SARS-CoV-2 infections, but with unknown reach from original infection to antigenically drifted variants. Although emerging SARS-CoV-2 variants of concern escape binding by many members of the groups associated with the most potent neutralizing activity, some antibodies in each of those groups retain affinity, suggesting that otherwise redundant components of a primary immune response are important for durable protection from evolving pathogens [ 57 ]. Scheid JF et al found that the SARS-CoV-2-specific B cell repertoire consists of transcriptionally distinct B cell populations with cells producing potently neutralizing antibodies (nAbs) localized in two clusters that resemble memory and activated B cells [ 58 ]. Together, our results characterize transcriptional differences among SARS-CoV-2-specific and SLE B cells and would provide reference for immunogen and therapeutic design against coronaviruses. Several limitations are existed in our study, and more comprehensive studies are needed. First, we used data from different studies based on the GEO database, which might cause unavoidable heterogeneity. Second, the exact molecular mechanisms of the genetic signatures need to be further identified by experiments and clinical practice in the future. Further explores are urged to confirm the functions of these DEGs in SLE and COVID-19. Our primary objective was to discuss the multifaceted impacts on the COVID-19 pandemic for SLE patients in transcriptomic and single-cell analyses. These theoretical perspectives lay a foundation for designing specific and effective therapeutic methods for fighting SARS-CoV-2. 5. Conclusions In summary, we analyzed the difference in COVID-19 and SLE from multiple dimensions. The systematic evaluation of characteristic differences between different subgroups in our study makes sense for the heterogeneity and treatment complexity of the two diseases. Genetic testing of the key immune gene CXCL10 and molecular characteristics might help promote the diagnosis and treatment of COVID-19 and SLE patients. Immune cell disorder appears in COVID-19 and SLE patients at the single-cell level and in blood tests. The significantly upregulated and downregulated genes in PBMCs and B-cell clusters of SLE and COVID-19 patients mainly included RACK1, ATP5E and AC090498.1. Moreover, we provided an opportunity to comprehensively probe underlying B-cell‒cell communication with multiple ligand‒receptor pairs (MIF-CD74 + CD44, MIF-CD74 + CXCR4) and the differentiation trajectory of B-cell clusters that can drive cell state heterogeneity and transitions in COVID-19 and SLE. Therefore, this study revealed the cellular and molecular associations of COVID-19 with SLE, especially for the synergistic complexities of COVID-19 and SLE, which might provide clues for personalized immunotherapy for SLE patients infected with COVID-19 in the future. Abbreviations coronavirus disease 2019 (COVID-19); systemic lupus erythematosus (SLE); peripheral blood mononuclear cells (PBMCs); severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); angiotensin converting enzyme 2 (ACE2); human leukocyte antigens (HLA); Single-cell RNA sequencing (scRNA-seq); single-sample gene set enrichment (ssGSEA); Gene set variation analysis (GSVA); weighted gene coexpression network analysis (WGCNA); principal component analysis (PCA); Molecular Signatures Database (MSigDB); Kyoto Encyclopedia of Genes and Genomes (KEGG); Uniform Manifold Approximation and Projection (UMAP); Gene set enrichment analysis (GSEA); Single-sample GSEA (ssGSEA); differential gene expression (DEGs); regulatory T cells (Treg); CD3-TCR complex (CD3D); SH2 domain-containing 1A (SH2D1A); SH2 domain-containing 1B (SH2D1B); C-X-C motif chemokine ligand 10 (CXCL10); Gene Ontology (GO); extracellular matrix (ECM); SH2 domain containing 1B (SH2D1B); granulysin (GNLY); acute respiratory distress syndrome (ARDS); antibody-mediated immunity (AMI); receptor for activated protein C kinase 1 (RACK1); Zika virus (ZIKV); membrane spanning 4-domains A1(MS4A1); CD79a molecule (CD79A) ; ATP synthase (ATP5E) ; ATP synthase F1 subunit epsilon (ATP5F1E). Declarations DATA AVAILABILITY STATEMENT Generated Statement: All analyses were performed based on publicly available datasets in this study. These datasets are available in the GEO database. The accession number(s) of datasets are included in the article. The information of supporting the findings of this study are publicly available through the supplementary materials, and other reasonable requests are available from the corresponding author. AUTHOR CONTRIBUTIONS All listed authors contribute substantially to this work and approved its publication. R.D. and Z.R. designed the study. X.Z. performed the data analysis and experimental validation. M.Z., Y.J., W.L., S.L. and C.G. conducted the screening of the public datasets. L.Z. and B.N. managed all the raw data. R.D. and W.L. edited the manuscript. Project funding was provided by X.Z. and R.D.. All authors have read and agreed to the publication of the work in this manuscript. CONFLICT OF INTEREST The authors report no conflicts of interest in this work. FUNDING This study was supported by Natural Science Foundation of Chongqing (cstc2021jcyj-msxmX0058 and CSTB2022NSCQ-MSX1428). ACKNOWLEDGMENTS We would like to thank the colleagues for useful discussions and comments. References Doaty S, Agrawal H, Bauer E, Furst DE. Infection and Lupus: Which Causes Which? Curr Rheumatol Rep. 2016; 18(3): 13.https://doi.org/10.1007/s11926-016-0561-4. Sawalha AH, Zhao M, Coit P, Lu Q. Epigenetic dysregulation of ACE2 and interferon-regulated genes might suggest increased COVID-19 susceptibility and severity in lupus patients . medRxiv. 2020.https://doi.org/10.1101/2020.03.30.20047852. Thanou A, Sawalha AH. SARS-CoV-2 and Systemic Lupus Erythematosus . Curr Rheumatol Rep. 2021; 23(2): 8.https://doi.org/10.1007/s11926-020-00973-w. Tiendrebeogo WJS, Kabore F, Diendere EA, Ouedraogo DD. Case Series of Chronic Inflammatory Rheumatic Disease Patients Infected by Coronavirus Disease 2019 (COVID-19) . Case Rep Rheumatol. 2020; 2020: 8860492.https://doi.org/10.1155/2020/8860492. Zacharias H, Dubey S, Koduri G, D'Cruz D. Rheumatological complications of Covid 19 . Autoimmun Rev. 2021; 20(9): 102883.https://doi.org/10.1016/j.autrev.2021.102883. Liu Y, Sawalha AH, Lu Q. COVID-19 and autoimmune diseases . Curr Opin Rheumatol. 2021; 33(2): 155-162.https://doi.org/10.1097/BOR.0000000000000776. Halpert G, Shoenfeld Y. SARS-CoV-2, the autoimmune virus . Autoimmun Rev. 2020; 19(12): 102695.https://doi.org/10.1016/j.autrev.2020.102695. Shayestehpour M, Zamani B. Systemic lupus erythematosus and varicella-like rash following COVID-19 in a previously healthy patient . J Med Virol. 2021; 93(5): 2599.https://doi.org/10.1002/jmv.26838. Spihlman AP, Gadi N, Wu SC, Moulton VR. COVID-19 and Systemic Lupus Erythematosus: Focus on Immune Response and Therapeutics . Front Immunol. 2020; 11: 589474.https://doi.org/10.3389/fimmu.2020.589474. Gralinski LE, Menachery VD. Return of the Coronavirus: 2019-nCoV . Viruses. 2020; 12(2).https://doi.org/10.3390/v12020135. Misra DP, Agarwal V, Gasparyan AY, Zimba O. Rheumatologists' perspective on coronavirus disease 19 (COVID-19) and potential therapeutic targets . Clin Rheumatol. 2020; 39(7): 2055-2062.https://doi.org/10.1007/s10067-020-05073-9. Katz-Agranov N, Zandman-Goddard G. Autoimmunity and COVID-19 - The microbiotal connection . Autoimmun Rev. 2021; 20(8): 102865.https://doi.org/10.1016/j.autrev.2021.102865. Gkogkou E, Barnasas G, Vougas K, Trougakos IP. Expression profiling meta-analysis of ACE2 and TMPRSS2, the putative anti-inflammatory receptor and priming protease of SARS-CoV-2 in human cells, and identification of putative modulators . Redox Biol. 2020; 36: 101615.https://doi.org/10.1016/j.redox.2020.101615. Zhou SY, Zhang C, Shu WJ, Chong LY, He J, Xu Z, et al. Emerging Roles of Coronavirus in Autoimmune Diseases . Arch Med Res. 2021; 52(7): 665-672.https://doi.org/10.1016/j.arcmed.2021.03.012. Dendrou CA, Petersen J, Rossjohn J, Fugger L. HLA variation and disease . Nat Rev Immunol. 2018; 18(5): 325-339.https://doi.org/10.1038/nri.2017.143. McHugh J. New evidence for the 'cusp theory' to explain HLA associations in SLE . Nat Rev Rheumatol. 2022; 18(10): 552.https://doi.org/10.1038/s41584-022-00835-y. Deb P, Zannat KE, Talukder S, Bhuiyan AH, Jilani MSA, Saif-Ur-Rahman KM. Association of HLA gene polymorphism with susceptibility, severity, and mortality of COVID-19: A systematic review . HLA. 2022; 99(4): 281-312.https://doi.org/10.1111/tan.14560. Langton DJ, Bourke SC, Lie BA, Reiff G, Natu S, Darlay R, et al. The influence of HLA genotype on the severity of COVID-19 infection . HLA. 2021; 98(1): 14-22.https://doi.org/10.1111/tan.14284. Papalexi E, Satija R. Single-cell RNA sequencing to explore immune cell heterogeneity . Nat Rev Immunol. 2018; 18(1): 35-45.https://doi.org/10.1038/nri.2017.76. Wang Y, Xie X, Zhang C, Su M, Gao S, Wang J, et al. Rheumatoid arthritis, systemic lupus erythematosus and primary Sjogren's syndrome shared megakaryocyte expansion in peripheral blood . Ann Rheum Dis. 2022; 81(3): 379-385.https://doi.org/10.1136/annrheumdis-2021-220066. Der E, Suryawanshi H, Morozov P, Kustagi M, Goilav B, Ranabothu S, et al. Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways . Nat Immunol. 2019; 20(7): 915-927.https://doi.org/10.1038/s41590-019-0386-1. He Y, Jiang Z, Chen C, Wang X. Classification of triple-negative breast cancers based on Immunogenomic profiling . J Exp Clin Cancer Res. 2018; 37(1): 327.https://doi.org/10.1186/s13046-018-1002-1. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data . BMC Bioinformatics. 2013; 14: 7.https://doi.org/10.1186/1471-2105-14-7. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies . Nucleic Acids Res. 2015; 43(7): e47.https://doi.org/10.1093/nar/gkv007. Bhattacharya S, Andorf S, Gomes L, Dunn P, Schaefer H, Pontius J, et al. ImmPort: disseminating data to the public for the future of immunology . Immunol Res. 2014; 58(2-3): 234-239.https://doi.org/10.1007/s12026-014-8516-1. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis . BMC Bioinformatics. 2008; 9: 559.https://doi.org/10.1186/1471-2105-9-559. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0 . Bioinformatics. 2011; 27(12): 1739-1740.https://doi.org/10.1093/bioinformatics/btr260. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters . OMICS. 2012; 16(5): 284-287.https://doi.org/10.1089/omi.2011.0118. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, 3rd, et al. Comprehensive Integration of Single-Cell Data . Cell. 2019; 177(7): 1888-1902 e1821.https://doi.org/10.1016/j.cell.2019.05.031. Zhang X, Lan Y, Xu J, Quan F, Zhao E, Deng C, et al. CellMarker: a manually curated resource of cell markers in human and mouse . Nucleic Acids Res. 2019; 47(D1): D721-D728.https://doi.org/10.1093/nar/gky900. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update . Nucleic Acids Res. 2016; 44(W1): W90-97.https://doi.org/10.1093/nar/gkw377. Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH, et al. Inference and analysis of cell-cell communication using CellChat . Nat Commun. 2021; 12(1): 1088.https://doi.org/10.1038/s41467-021-21246-9. Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes . Nat Protoc. 2020; 15(4): 1484-1506.https://doi.org/10.1038/s41596-020-0292-x. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells . Nat Biotechnol. 2014; 32(4): 381-386.https://doi.org/10.1038/nbt.2859. Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA, et al. Reversed graph embedding resolves complex single-cell trajectories . Nat Methods. 2017; 14(10): 979-982.https://doi.org/10.1038/nmeth.4402. Ramalho R, Rao M, Zhang C, Agrati C, Ippolito G, Wang FS, et al. Immunometabolism: new insights and lessons from antigen-directed cellular immune responses . Semin Immunopathol. 2020; 42(3): 279-313.https://doi.org/10.1007/s00281-020-00798-w. Lercher A, Baazim H, Bergthaler A. Systemic Immunometabolism: Challenges and Opportunities . Immunity. 2020; 53(3): 496-509.https://doi.org/10.1016/j.immuni.2020.08.012. Palsson-McDermott EM, O'Neill LAJ. Targeting immunometabolism as an anti-inflammatory strategy . Cell Res. 2020; 30(4): 300-314.https://doi.org/10.1038/s41422-020-0291-z. Thomas R, Wang W, Su DM. Contributions of Age-Related Thymic Involution to Immunosenescence and Inflammaging . Immun Ageing. 2020; 17: 2.https://doi.org/10.1186/s12979-020-0173-8. Omarjee L, Perrot F, Meilhac O, Mahe G, Bousquet G, Janin A. Immunometabolism at the cornerstone of inflammaging, immunosenescence, and autoimmunity in COVID-19 . Aging (Albany NY). 2020; 12(24): 26263-26278.https://doi.org/10.18632/aging.202422. Lee EY, Lee ZH, Song YW. CXCL10 and autoimmune diseases . Autoimmun Rev. 2009; 8(5): 379-383.https://doi.org/10.1016/j.autrev.2008.12.002. Zhang N, Zhao YD, Wang XM. CXCL10 an important chemokine associated with cytokine storm in COVID-19 infected patients . Eur Rev Med Pharmacol Sci. 2020; 24(13): 7497-7505.https://doi.org/10.26355/eurrev_202007_21922. Sinha D, Kumar A, Kumar H, Bandyopadhyay S, Sengupta D. dropClust: efficient clustering of ultra-large scRNA-seq data . Nucleic Acids Res. 2018; 46(6): e36.https://doi.org/10.1093/nar/gky007. Oliviero A, de Castro F, Coperchini F, Chiovato L, Rotondi M. COVID-19 Pulmonary and Olfactory Dysfunctions: Is the Chemokine CXCL10 the Common Denominator? Neuroscientist. 2021; 27(3): 214-221.https://doi.org/10.1177/1073858420939033. Blot M, Jacquier M, Glele LA, Beltramo G, Nguyen M, Bonniaud P, et al. Correction to: CXCL10 could drive longer duration of mechanical ventilation during COVID-19 ARDS . Crit Care. 2021; 25(1): 143.https://doi.org/10.1186/s13054-021-03559-9. Li S, Duan X, Li Y, Li M, Gao Y, Li T, et al. Differentially expressed immune response genes in COVID-19 patients based on disease severity . Aging (Albany NY). 2021; 13(7): 9265-9276.https://doi.org/10.18632/aging.202877. Zhang R, Li Y, Pan B, Li Y, Liu A, Li X. Increased expression of hub gene CXCL10 in peripheral blood mononuclear cells of patients with systemic lupus erythematosus . Exp Ther Med. 2019; 18(5): 4067-4075.https://doi.org/10.3892/etm.2019.8013. Welcher AA, Boedigheimer M, Kivitz AJ, Amoura Z, Buyon J, Rudinskaya A, et al. Blockade of interferon-gamma normalizes interferon-regulated gene expression and serum CXCL10 levels in patients with systemic lupus erythematosus . Arthritis Rheumatol. 2015; 67(10): 2713-2722.https://doi.org/10.1002/art.39248. Tang W, Gartshteyn Y, Ricker E, Inzerillo S, Murray S, Khalili L, et al. The Use of COVID-19 Vaccines in Patients with SLE . Curr Rheumatol Rep. 2021; 23(11): 79.https://doi.org/10.1007/s11926-021-01046-2. Tavasolian F, Rashidi M, Hatam GR, Jeddi M, Hosseini AZ, Mosawi SH, et al. HLA, Immune Response, and Susceptibility to COVID-19 . Front Immunol. 2020; 11: 601886.https://doi.org/10.3389/fimmu.2020.601886. Huang X, Luu LDW, Jia N, Zhu J, Fu J, Xiao F, et al. Multi-Platform Omics Analysis Reveals Molecular Signatures for Pathogenesis and Activity of Systemic Lupus Erythematosus . Front Immunol. 2022; 13: 833699.https://doi.org/10.3389/fimmu.2022.833699. Ambegaonkar AA, Holla P, Dizon BL, Sohn H, Pierce SK. Atypical B cells in chronic infectious diseases and systemic autoimmunity: puzzles with many missing pieces . Curr Opin Immunol. 2022; 77: 102227.https://doi.org/10.1016/j.coi.2022.102227. Dizon BLP, Pierce SK. The tangled web of autoreactive B cells in malaria immunity and autoimmune disease . Trends Parasitol. 2022; 38(5): 379-389.https://doi.org/10.1016/j.pt.2022.01.005. Shue B, Chiramel AI, Cerikan B, To TH, Frolich S, Pederson SM, et al. Genome-Wide CRISPR Screen Identifies RACK1 as a Critical Host Factor for Flavivirus Replication . J Virol. 2021; 95(24): e0059621.https://doi.org/10.1128/JVI.00596-21. Tarke A, Coelho CH, Zhang Z, Dan JM, Yu ED, Methot N, et al. SARS-CoV-2 vaccination induces immunological T cell memory able to cross-recognize variants from Alpha to Omicron . Cell. 2022; 185(5): 847-859 e811.https://doi.org/10.1016/j.cell.2022.01.015. Feldman J, Bals J, Altomare CG, St Denis K, Lam EC, Hauser BM, et al. Naive human B cells engage the receptor binding domain of SARS-CoV-2, variants of concern, and related sarbecoviruses . Sci Immunol. 2021; 6(66): eabl5842.https://doi.org/10.1126/sciimmunol.abl5842. Tong P, Gautam A, Windsor IW, Travers M, Chen Y, Garcia N, et al. Memory B cell repertoire for recognition of evolving SARS-CoV-2 spike . Cell. 2021; 184(19): 4969-4980 e4915.https://doi.org/10.1016/j.cell.2021.07.025. Scheid JF, Barnes CO, Eraslan B, Hudak A, Keeffe JR, Cosimi LA, et al. B cell genomics behind cross-neutralization of SARS-CoV-2 variants and SARS-CoV . Cell. 2021; 184(12): 3205-3221 e3224.https://doi.org/10.1016/j.cell.2021.04.032. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial2.pdf Cite Share Download PDF Status: Published Journal Publication published 19 Sep, 2023 Read the published version in Inflammation Research → Version 1 posted Editorial decision: Major revision 07 Jun, 2023 Reviews received at journal 03 Jun, 2023 Reviewers agreed at journal 24 May, 2023 Reviewers invited by journal 23 May, 2023 Submission checks completed at journal 15 May, 2023 Editor assigned by journal 15 May, 2023 First submitted to journal 13 May, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2932364","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":200540373,"identity":"1e9b933c-d1e8-4318-a555-fb3a45b95cb2","order_by":0,"name":"Xingwang Zhao","email":"","orcid":"","institution":"Army Medical University (Third Military Medical University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xingwang","middleName":"","lastName":"Zhao","suffix":""},{"id":200540374,"identity":"f26af07e-5c16-4cba-a4da-2e2cb628b3f1","order_by":1,"name":"Mengjie Zhang","email":"","orcid":"","institution":"Third Military Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mengjie","middleName":"","lastName":"Zhang","suffix":""},{"id":200540375,"identity":"09e0e29a-6830-4f02-9b7f-abfefe64eb98","order_by":2,"name":"Yuying Jia","email":"","orcid":"","institution":"the 901th Hospital of the Joint Logistics Support Force of PLA, Anhui Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuying","middleName":"","lastName":"Jia","suffix":""},{"id":200540376,"identity":"42dfd2af-74c3-4aa0-86fc-d55a34939279","order_by":3,"name":"Wenying Liu","email":"","orcid":"","institution":"Army Medical University (Third Military Medical University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenying","middleName":"","lastName":"Liu","suffix":""},{"id":200540377,"identity":"09d77913-7d29-4038-a97b-418daf61e72c","order_by":4,"name":"Shifei Li","email":"","orcid":"","institution":"Army Medical University (Third Military Medical University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shifei","middleName":"","lastName":"Li","suffix":""},{"id":200540378,"identity":"b3bf5291-6c2f-47f4-9f93-8d9511d85258","order_by":5,"name":"Cuie Gao","email":"","orcid":"","institution":"Army Medical University (Third Military Medical University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cuie","middleName":"","lastName":"Gao","suffix":""},{"id":200540379,"identity":"4719aae0-d95e-4ac8-bb95-f34f32868484","order_by":6,"name":"Lian Zhang","email":"","orcid":"","institution":"Army Medical University (Third Military Medical University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lian","middleName":"","lastName":"Zhang","suffix":""},{"id":200540380,"identity":"90ee5e78-d393-4b71-a5de-5ec8fe3fa734","order_by":7,"name":"Bing Ni","email":"","orcid":"","institution":"Third Military Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Ni","suffix":""},{"id":200540381,"identity":"3b02b577-8ef0-4fe3-a21b-35fd595362bc","order_by":8,"name":"Zhihua Ruan","email":"","orcid":"","institution":"Army Medical University, Third Military Medical University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhihua","middleName":"","lastName":"Ruan","suffix":""},{"id":200540382,"identity":"1b5dabfb-6cea-44f0-b7ca-f839440c1309","order_by":9,"name":"Rui Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYDCCA2BSgoGBvbHx4QfStPAcbjaWIEELSFd6mwAPMTr4jvcefl3ZZiHPd/NhG9AyOzndBgJaJM+cS7M82yZhOPN2YtuDAoZkY7MDBLQY3MgxM2xsk0gwuJ3YbiDBcCBxG/Fabh5sk+AhUovxQ7CWG4xEapE8c8aMseEc0C9nEoGBbECEX/iO9xh/bCirk+c7fvzhww8VdnIEtQABGyQCwSoNCCsHAeYPCC2jYBSMglEwCrAAADi+RqWUrQriAAAAAElFTkSuQmCC","orcid":"","institution":"Chongqing International Institute for Immunology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2023-05-14 02:44:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2932364/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2932364/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00011-023-01791-3","type":"published","date":"2023-09-19T15:00:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":37188487,"identity":"7594c08e-9375-460d-8023-3efc575f0426","added_by":"auto","created_at":"2023-05-18 12:45:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1205417,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of immunological characteristics and immune-related DEGs in GSE157103.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Unsupervised clustering of immune cell infiltration between the immune_H and immune_L subtypes (blue: low expression; red: high expression). The upper columns include ESTIMATEScore, ImmuneScore, StromalScore, and Subtype. \u003cstrong\u003e(B)\u003c/strong\u003e TME scores between the two groups. \u003cstrong\u003e(C) \u003c/strong\u003eExpression of HLA genes between both groups. \u003cstrong\u003e(D)\u003c/strong\u003eImmune cell fraction between both groups. \u003cstrong\u003e(E)\u003c/strong\u003eThe overlapped genes between DEGs and immunity-related genes.\u003cstrong\u003e (F)\u003c/strong\u003e GSEA shows the significantly enriched biological processes in the GSE157103 cohort. \u003cstrong\u003e(G)\u003c/strong\u003e Heatmap of the correlation between the clinical characteristics and module eigengenes.WGCNA was adopted to identify the gene modules. Red: strongly correlated modules (|Cor| \u0026gt; 0.5, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05); Blue: weakly correlated modules (|Cor| \u0026lt; 0.5, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ns: no significance.\u003c/p\u003e","description":"","filename":"FIG.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2932364/v1/51da96ccc0f35b4b9f5a2a26.jpg"},{"id":37188486,"identity":"baabf04a-96ec-4dc9-a81f-e76f57220e1e","added_by":"auto","created_at":"2023-05-18 12:45:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1521465,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of immunological characteristics and immunity-related DEGs in GSE72509.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eUnsupervised clustering of immune cell infiltration between the immune_H and immune_L subtypes (the red represents high expression and the blue represents low expression). The upper columns include ESTIMATEScore, ImmuneScore, StromalScore, and Subtype. \u003cstrong\u003e(B)\u003c/strong\u003eTME score between both groups. \u003cstrong\u003e(C) \u003c/strong\u003eExpression of HLA genes between both groups. \u003cstrong\u003e(D)\u003c/strong\u003e Immune cell fraction between both groups. \u003cstrong\u003e(E)\u003c/strong\u003e The overlapping genes between DEGs and immune-related genes. \u003cstrong\u003e(F)\u003c/strong\u003eHeatmap of the correlation between the clinical characteristics and module eigengenes. WGCNA was used to identify the gene modules. Red: strongly correlated modules (|Cor| \u0026gt; 0.5, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05); Blue: weakly correlated modules (|Cor| \u0026lt; 0.5, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). \u003cstrong\u003e(G)\u003c/strong\u003e GSEA shows the significantly enriched biological processes in the GSE72509 dataset. \u003cstrong\u003e(H) \u003c/strong\u003eVenn diagram showing overlapped genes between DEGs and immune genes in the six cohorts. *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ns: no significance.\u003c/p\u003e","description":"","filename":"FIG.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2932364/v1/46d51f3e3f2e3ef10f8d408c.jpg"},{"id":37188492,"identity":"a0e74d1e-dbf2-4e55-a0ea-6888ce0173f8","added_by":"auto","created_at":"2023-05-18 12:45:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3268886,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of molecular subtype characteristics in the COVID-19 and SLE cohorts.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e PCA was performed to distinguish the GEO cohorts of COVID-19 (GSE157103, GSE161731 and GSE163151). \u003cstrong\u003e(B, C)\u003c/strong\u003e Differences in the proportion of males\u003cstrong\u003e (B)\u003c/strong\u003e and age \u003cstrong\u003e(C)\u003c/strong\u003e between 3 subgroups of COVID-19 cohorts (Wilcoxon test) are shown. \u003cstrong\u003e(D)\u003c/strong\u003e Gene dendrogram revealing the 13 modules identified by average linkage hierarchical clustering by WGCNA. \u003cstrong\u003e(E)\u003c/strong\u003eHeatmap of the correlation between the clinical characteristics and module eigengenes. Red: strongly correlated modules (|Cor| \u0026gt; 0.5, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05); Blue: weakly correlated modules (|Cor| \u0026lt; 0.5, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). \u003cstrong\u003e(F)\u003c/strong\u003e Biological pathways in distinct subgroups of COVID-19 was shown in the heatmap. The upper columns consist of subtype and term. The k = 3 was selected as the optimal cluster number, CDF curves k = 2-10. \u003cstrong\u003e(G) \u003c/strong\u003ePCA was used to distinguish from the GEO datasets of SLE (GSE72509, GSE49454 and GSE110169). \u003cstrong\u003e(H)\u003c/strong\u003e Differences in the proportion of females between the 3 subgroups of the SLE cohorts. \u003cstrong\u003e(I)\u003c/strong\u003e Gene dendrogram showing the modules identified by average linkage hierarchical clustering.\u003cstrong\u003e (J)\u003c/strong\u003e A heatmap was used to visualize biological pathways in distinct subgroups of SLE patients. The control group represents healthy controls. The upper columns consist of subtype and term. The k = 3 was selected as the optimal cluster number, CDF curves k = 2-10. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ns: no significance.\u003c/p\u003e","description":"","filename":"FIG.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2932364/v1/4b96e19f39515c70e64519cb.jpg"},{"id":37188490,"identity":"db12f0a6-3e87-42d2-b74c-fba9eb507fae","added_by":"auto","created_at":"2023-05-18 12:45:54","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1836853,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell immune transcription atlas of PBMCs in SLE and COVID-19\u003c/strong\u003e \u003cstrong\u003esamples.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e UMAP plot of 48961 singlecells from PBMCs. Each cell type is highlighted with a different color. \u003cstrong\u003e(B)\u003c/strong\u003e UMAP plot of SLE and COVID-19 groups. \u003cstrong\u003e(C) \u003c/strong\u003eExpression of selective marker genes for cell clusters and the cell positions are in the UMAP plot of panels.\u003cstrong\u003e(D)\u003c/strong\u003e UMAP plot of 48961 single cells from PBMCs. Each cell type is highlighted with a different color. \u003cstrong\u003e(E)\u003c/strong\u003e Dot plot of cell specified type marker genes. The dot size shows the average percentage, and the dot color shows the average expression of indicated gene. \u003cstrong\u003e(F)\u003c/strong\u003eHeatmap shows the top 3 DEGs in each cell subset. Yellow represents high expression.\u003cstrong\u003e (G) \u003c/strong\u003eThe cell counts and percent of each cluster were detected in PBMCs of the SLE and COVID-19 groups.\u003c/p\u003e","description":"","filename":"FIG.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2932364/v1/29954e453a8612f2c005582b.jpg"},{"id":37190888,"identity":"7f7ce7fe-e896-45d0-ab64-06c16962ca02","added_by":"auto","created_at":"2023-05-18 13:01:54","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":761919,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic features of each cluster.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e The scatter plot displays gene expression across all PBMCs in COVID-19 vs. SLE samples. \u003cstrong\u003e(B)\u003c/strong\u003e UMAP plots of classical cell markers in cell clusters and the color indicated expression levels (gray:low, red: high). \u003cstrong\u003e(C, D)\u003c/strong\u003e The expression levels of representative signatures in 21 cell subtypes. Violin plots show the differential expression of RACK1 and ATP5E genes in each cluster of cells from SLE and COVID-19 patients. \u003cstrong\u003e(E-J) \u003c/strong\u003eVolcano plot showing the DEGs in cell clusters of COVID-19 versus SLE, including \u003cstrong\u003e(E)\u003c/strong\u003e CD4\u003csup\u003e+\u003c/sup\u003e naive T cells, \u003cstrong\u003e(F)\u003c/strong\u003e naive B1 cells, \u003cstrong\u003e(G)\u003c/strong\u003e CD4\u003csup\u003e+\u003c/sup\u003e memory T cells, \u003cstrong\u003e(H)\u003c/strong\u003e CD8\u003csup\u003e+ \u003c/sup\u003eeffector T cells, \u003cstrong\u003e(I) \u003c/strong\u003eCD4\u003csup\u003e+ \u003c/sup\u003eT cells, and \u003cstrong\u003e(J)\u003c/strong\u003e CD8\u003csup\u003e+\u003c/sup\u003e naive T cells.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig5new.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2932364/v1/d177c8c440f602cd3a2b69e3.jpg"},{"id":37189976,"identity":"8f3c7182-cf49-4b03-b909-252d4e1b3d76","added_by":"auto","created_at":"2023-05-18 12:53:54","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1550846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell transcription atlas of B cells in SLE and COVID-19.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e UMAP plot of 27745 single cells from all B cells. Different cell types are highlighted with different color. \u003cstrong\u003e(B)\u003c/strong\u003e The cell counts and percent of each cluster were detected in B cells of the SLE and COVID-19 groups. \u003cstrong\u003e(C) \u003c/strong\u003eExpression levels of the selected marker genes of cell clusters, and the cell positions are shown in the UMAP plot. \u003cstrong\u003e(D)\u003c/strong\u003e Dot plot of specified cell type marker genes. The dot size shows the average percentage, and the dot color shows the average expression of indicated gene. \u003cstrong\u003e(E)\u003c/strong\u003e Heatmap shows the top 3 DEGs in each cell subset. Yellow represents high expression.\u003c/p\u003e","description":"","filename":"FIG.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2932364/v1/78bc25bec9f0d3532108a515.jpg"},{"id":37189974,"identity":"3901ea3c-49df-4497-989a-6031a12d7b4d","added_by":"auto","created_at":"2023-05-18 12:53:54","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":867477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic features of each B-cell cluster.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e UMAP plots of cell clusters labeling with cell markers, color represented expression levels (gray: low, red: high). \u003cstrong\u003e(B, C)\u003c/strong\u003e The expression levels of representative signatures in the two subtypes. \u003cstrong\u003e(D-M)\u003c/strong\u003e Volcano plots showing the differentially expressed genes of B-cell clusters in COVID-19 versus SLE\u003cstrong\u003e (D)\u003c/strong\u003e, S100A4\u003csup\u003e+\u003c/sup\u003e B, TCL1A\u003csup\u003e+\u003c/sup\u003e naive B1 \u003cstrong\u003e(E)\u003c/strong\u003e, TCL1A\u003csup\u003e+\u003c/sup\u003e naive B2 \u003cstrong\u003e(F)\u003c/strong\u003e, CCL5\u003csup\u003e+\u003c/sup\u003e B \u003cstrong\u003e(G)\u003c/strong\u003e, CD4B\u003csup\u003e+ \u003c/sup\u003eB \u003cstrong\u003e(H)\u003c/strong\u003e, CD83\u003csup\u003e+\u003c/sup\u003e B \u003cstrong\u003e(I)\u003c/strong\u003e, CXCR5\u003csup\u003e+\u003c/sup\u003e B \u003cstrong\u003e(J)\u003c/strong\u003e, HLA-DRB5\u003csup\u003e+\u003c/sup\u003e B \u003cstrong\u003e(K)\u003c/strong\u003e, IGHV3\u003csup\u003e+\u003c/sup\u003e B\u003cstrong\u003e (L)\u003c/strong\u003e, LYZ\u003csup\u003e+\u003c/sup\u003e B\u003cstrong\u003e (M)\u003c/strong\u003e, and MT\u003csup\u003e+\u003c/sup\u003e B cells.\u003c/p\u003e","description":"","filename":"Fig7new.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2932364/v1/fc58359640323c8fcfc1d109.jpg"},{"id":37188494,"identity":"be150ae1-85f7-45f4-8573-075a5f399a91","added_by":"auto","created_at":"2023-05-18 12:45:54","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1270666,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePseudotime trajectory analysis of B cells among SLE and COVID-19patients based on Monocle.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A, B)\u003c/strong\u003e Potential differentiation routines among SLE and COVID-19. \u003cstrong\u003e(C) \u003c/strong\u003eHeatmap shows that the genes were markedly differentially expressed along the pseudotime axis in the SLE samples. These genes were further clustered into 11 modules according to the expression patterns. \u003cstrong\u003e(D)\u003c/strong\u003e Pseudotemporal expression dynamics of specific representative genes in the SLE samples. \u003cstrong\u003e(E) \u003c/strong\u003eHeatmap shows that the genes were markedly differentially expressed along the pseudotime axis in the COVID-19 samples. These genes were further clustered into 15 modules according to their expression patterns. \u003cstrong\u003e(F)\u003c/strong\u003e Pseudotemporal expression dynamics of specific representative genes in COVID-19. \u003cstrong\u003e(G, H)\u003c/strong\u003e Pseudotime projections and cell trajectory projections of transcriptional changes of immune-related genes.\u003c/p\u003e","description":"","filename":"FIG.8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2932364/v1/2e43e53c13f99126f9f57d2f.jpg"},{"id":37189977,"identity":"3725b431-f56f-4a44-ab58-7ead99b81155","added_by":"auto","created_at":"2023-05-18 12:53:54","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":942755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisualization and analysis of B-cell‒cell communication of COVID-19 and SLE.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eCircle plot: SLE. \u003cstrong\u003e(B) \u003c/strong\u003eCircle plot: COVID-19. \u003cstrong\u003e(C, D)\u003c/strong\u003e Hierarchical plot of cell-cell communication network about the MIF signaling pathway. Solid circles indicate the source and open circles indicate the target. \u003cstrong\u003e(E, F)\u003c/strong\u003e Heatmap showing the relative importance of each B-cell group according to the four computed network centrality measures of the MIF signaling network. \u003cstrong\u003e(G, H) \u003c/strong\u003eThe bubble plot shows that the signaling from the B-cell cluster to other B clusters through the interaction of ligand‒receptor pairs.\u003c/p\u003e","description":"","filename":"FIG.9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2932364/v1/be4c3e239e651f279525ecc2.jpg"},{"id":43640981,"identity":"d7937855-a140-44af-af40-51faf56f1558","added_by":"auto","created_at":"2023-09-25 15:09:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2293505,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2932364/v1/42f95072-fbf7-4156-8af5-55df8c7c42af.pdf"},{"id":37188495,"identity":"137bcbd0-ea4c-4a11-8dd5-f98553e88dba","added_by":"auto","created_at":"2023-05-18 12:45:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5321808,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2932364/v1/a9bee1578458c93faaa15ade.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Featured immune characteristics of COVID-19 and systemic lupus erythematosus revealed by multidimensional integrated analyses","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoronavirus disease 2019 (COVID-19) is an infectious disease, which is attribute to infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has recently spread rapidly worldwide and led to great human hardship. Systemic lupus erythematosus (SLE) is a chronic relapsing multisystem autoimmune disease of unknown etiology, and patients with SLE may have a high risk of acquiring SARS-CoV-2 infection [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The coronavirus shares a common host cell entry receptor, angiotensin converting enzyme 2 (ACE2), of which the aberrant upregulation has been observed in T cells from SLE patients [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Patients with SLE are susceptible to SARS-CoV-2 infection due to innate immune disorders and increased inflammation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Once infected with coronavirus, SLE patients exhibit severely weakened and altered immune systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, SARS-CoV-2 infections can induce other autoimmune diseases including autoimmune kidney disease as well as some rheumatic complications [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have reported that coronaviruses may be correlated with the pathogenesis of autoimmune diseases. Some specific inflammatory and immune responses are involved in the pathogenesis of both COVID-19 and autoimmune disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Several similarities between COVID-19 and autoimmune diseases such as SLE mainly include aberrant immune responses such as proinflammatory cytokines, lymphopenia, and aberrant T- and B-cell responses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To some extent, organ injury in COVID-19 patients correlates with host immune status, as observed in patients with SLE [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, SLE and COVID-19 share some of the same complications including leukopenia, lymphocytopenia, thrombocytopenia, and vasculitis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Some autoantibodies associated with SLE can also be detected in SARS-CoV-2-infected patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, the microbiome characteristics of COVID-19 patients are similar to those of SLE patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. All these findings suggest that crosstalk exists between implicated inflammatory pathways/mechanisms and clinical characteristics in both SLE and COVID-19 patients.\u003c/p\u003e \u003cp\u003eHowever, there are few reports on the common molecular mechanisms of COVID-19 and SLE [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The mechanisms underlying SARS-CoV-2 infection modulates the risk of autoimmune disease including SLE has not been fully elucidated [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Human leukocyte antigens (HLA) molecules have been shown to play an important role in protective immunity as well as in disease-causing autoimmune responses [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. New evidences have shown that the HLA gene sets were associated with SLE [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The HLA gene sets, as part of host genetic factors, contribute to disease susceptibility and prognosis. HLA is crucial for appropriate immune responses in SARS CoV-2 infections [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A recent study provides evidence that HLA genotype influences clinical outcome in COVID-19 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We thus investigated the association of HLA gene sets with SLE and COVID-19 in this study. Moreover, the efficiency and safety of COVID-19 vaccines for the prevention of COVID-19 in SLE patients is unclear, especially in the patients receiving immunosuppressive treatment. In many cases, the current understanding of peripheral blood immune cells in SLE patients infected with COVID-19, which may be modulated during disease, is limited to flow cytometry assay, but the specific status of immune cells such as T and B cells, remains unclear. Single-cell transcriptome analysis facilitates discrimination of the disease specific immune status. Single-cell RNA sequencing (scRNA-seq) methods through the unbiased clustering of cell populations can obviously capture potential pathogenic drivers and biological targets [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which have been utilized to discriminate specific inflammatory cell states and identify specific immune cell populations in healthy subjects or diseased patients such as those with SLE [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, investigating the potential associations and molecular mechanisms between COVID-19 and SLE by analyzing high-throughput sequencing data is particularly urgent to help in diagnosing and treating both diseases.\u003c/p\u003e \u003cp\u003eIn this study, the distinct immune subtypes and molecular subtype pattern characteristics of COVID-19 and SLE cohorts were recognized. We also explored the transcriptional features of human peripheral blood mononuclear cells (PBMCs) from COVID-19 and SLE patients at the single-cell level to reveal the molecular mechanisms of specific disease processes. We then analyzed the single-cell immunological landscape to reveal immune cell types that might contribute to immunotherapy development. The key immune-related gene regulators in our results may serve as good biomarkers for diagnosing and monitoring the efficacy of immunotherapy. The common molecular pathway characteristics of the two diseases revealed in our study may also help scientists develop specific COVID-19 therapies and vaccines.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Datasets acquisition and preprocessing\u003c/h2\u003e\n \u003cp\u003eNormalized microarray datasets were acquired from the Gene Expression Omnibus database (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e). Six transcriptomic RNA sequencing datasets, namely, GSE157103 (whole blood, COVID-19), GSE161731 (whole blood, COVID-19), GSE163151 (whole blood, COVID-19), GSE72509 (whole blood, SLE), GSE49454 (whole blood, SLE) and GSE110169 (whole blood, SLE), and six single-cell transcriptomic RNA sequencing datasets, namely, GSE135779 (PBMCs, SLE), GSE142016 (PBMCs, SLE), GSE155222 (PBMCs, COVID-19), GSE166992 (PBMCs, COVID-19), GSE163121 (B cells, SLE), and GSE164379 (B cells, COVID-19), were obtained. The relevant information of the datasets in this study is displayed in \u003cstrong\u003eSupplementary Table\u0026nbsp;1 (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/strong\u003e. The clinical characteristics of the COVID-19 patients, SLE patients and healthy controls are shown in \u003cstrong\u003eTables S2 and S3\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Verification of immunity-related subtypes and differentially expressed genes\u003c/h2\u003e\n \u003cp\u003eBy using single-sample gene set enrichment (ssGSEA) of \u0026ldquo; Gene set variation analysis (GSVA) \u0026rdquo; R package to quantify the enrichment levels of the 29 immune-associated maker gene sets in each sample, which covered diverse immune cell types, functions, and pathways, and performed hierarchical clustering of samples, we evaluated the immune infiltration landscape of SLE and COVID-19 patients from datasets and the immune status of the subjects was divided into two immunophenotype groups (low immunity (immune_L) and high immunity (immune_H)) [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. The immune-related differentially expressed genes (DEGs) were determined by expression comparison between the two immunophenotype groups using the limma package of R software (adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC| \u0026gt; 1) [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. The gene sets with immune-relation were downloaded from the Immunology Database and Analysis Portal (ImmPort) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.immport.org\u003c/span\u003e\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, the relationship between the immune microenvironment and HLA gene expression in the two groups was analyzed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 WGCNA\u003c/h2\u003e\n \u003cp\u003eWeighted gene coexpression network analysis (WGCNA) is an R package for weighted correlation network analysis [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. We conducted hierarchical clustering of samples to cluster modules. The dynamic cutting algorithm was used to validate the gene modules. Subsequently, the characteristic genes were clustered and merged into modules. The gene network can be divided into different modules according to the similarity of expression, the leftmost color block represents the different gene modules and the rightmost color bar represents the correlation range. In the middle part of the heat map, the darker the color, the higher the correlation. Red indicates positive correlation, and green indicates negative correlation. The numbers in each cell indicate relevance and significance. Module-trait relationships between each module and the clinical characteristics were identified based on Pearson correlation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Functional annotation with molecular subtypes\u003c/h2\u003e\n \u003cp\u003eThree COVID-19 and SLE cohorts were used to develop the molecular subtype classification. Principal component analysis (PCA) was used for data dimensionality reduction and visualization.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Gene set variation analysis (GSVA)\u003c/h2\u003e\n \u003cp\u003eWe used the GSVA R package to study the activity of biological pathways among molecular subtypes [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. The \u0026lsquo;c2.cp.kegg.v7.0.symbols\u0026rsquo; gene sets were obtained from Release 6.0 of the Molecular Signatures Database (MSigDB) for GSVA analyses [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. The clusterProfiler R package was used to conduct Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses between distinct subtypes [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Cell clustering annotation and visualization\u003c/h2\u003e\n \u003cp\u003eThe Seurat v4.0.0 package of R software (v4.0.3) was used to perform the unsupervised clustering [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. The data were normalized to transcript copies per 10,000 and log-normalized to reduce sequencing depth variability. After quality control and filtering, the variation coefficient of genes was analyzed with Seurat, and 2000 genes were chosen for further analysis. Uniform Manifold Approximation and Projection (UMAP) plots was adopted for cluster identification and dimensionality reduction of visualization. The FindConservedMarkers was adopted for identification of the clusters. The parameters setting criteria are logFC.threshold\u0026thinsp;\u0026gt;\u0026thinsp;0.25, minPct\u0026thinsp;\u0026gt;\u0026thinsp;0.25 and Padj\u0026thinsp;\u0026le;\u0026thinsp;0.05. Then, clusters were annotated based on the DEGs and the canonical cellular markers according to the Cell Marker database [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Gene set enrichment analysis (GSEA) and Single-sample GSEA (ssGSEA)\u003c/h2\u003e\n \u003cp\u003eThe immune status in COVID-19 and SLE was evaluated by ssGSEA. The enrichment of a gene set was indicated by the enrichment fraction in each sample. The pathway enrichment analysis was performed using the GSEA in patients with different subtypes of COVID-19 or SLE according to the MSigDB. GO and KEGG pathway analyses were also conducted with GSEA and a false discovery rate (FDR)\u0026thinsp;\u0026le;\u0026thinsp;0.05 was defined as obviously enriched [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. The circus plots were drawn by using R v4.0.3.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8 Intercellular communication analysis\u003c/h2\u003e\n \u003cp\u003eTo study potential intercellular communication between the B-cell subsets, we used Cell Chat package in R to evaluate ligand-receptor expression and distribution of B-cell subsets through standard pipelines as described previously [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. We used Cell Phone DB (ligand-receptor pair list, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cellphonedb.org\u003c/span\u003e\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e] to identify cell‒cell interactions between B-cell subsets.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.9 Trajectory analysis\u003c/h2\u003e\n \u003cp\u003eThe cellular dynamic processes of B-cell subpopulation differentiation [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e] and the origination were modeled using a trajectory inference method. Specifically, the differential GeneTest [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] was used to calculate the top 150 signature genes. Monocle default parameters were used to infer B-cell differentiation trajectories after dimensionality reduction and cell ordering. The \u0026lsquo;DDRTree\u0026rsquo; and \u0026lsquo;plot_cell_trajectory\u0026rsquo; were utilized for dimensionality reduction and for visualization, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eThe software R v4.0.3 and corresponding R packages (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioconductor.org/\u003c/span\u003e\u003c/span\u003e) were used to perform statistical analysis. Spearman\u0026rsquo;s correlation was utilized to analyze correlations. The comparisons between two groups were examined by means of Wilcoxon test. All statistical \u003cem\u003eP\u003c/em\u003e values (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) represents statistical significance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Immune status and immune-related DEGs in COVID-19 and SLE patients\u003c/h2\u003e\n \u003cp\u003eWe searched six gene expression datasets representing samples from COVID-19 and SLE patients and samples from normal controls to start the analyses. We analyzed the immune status using the ssGSEA method, and performed clustering by dividing the COVID-19 patients into two groups, the immune_H group and immune_L group. Analysis of the difference in the distributions between both groups indicated that the stromal scores of the immune_H group were higher (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) based on the Mann-Whitney U test, consistent with the estimate scores and immune scores, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB. We also analyzed the relationship between both immune types and HLA gene sets. The results demonstrated that the samples in the immune_H group showed 12 genes (HLA-DMA, HLA-DMB, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DPB2, HLA-DQA1, HLA-DQA2, HLA-DQB2, HLA-DRA, HLA-DRB6, HLA-L) with higher expression and 6 genes (HLA-A, HLA-C, HLA-E, HLA-B, HLA-F and HLA-J) with significantly lower expression than those in the immune_L group (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). Furthermore, the immune_L group had more neutrophils while the immune_H group had more resting NK cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). Ninety-eight immune-related DEGs were found between the both groups (\u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/strong\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). In addition, GSEA analysis of the immune-related genes showed that COVID-19 patients were positively associated with allograft rejection biological processes (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF). WGCNA was used to describe the associations between the module eigengenes and clinical characteristics. The analysis confirmed that the pink module had significant positive correlation with hospital-free days and that the blue module had negative correlation with mechanical ventilation (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eG).\u003c/p\u003e\n \u003cp\u003eWe also divided the SLE patients into immune-high and -low groups by hierarchical clustering. That is the immune_H group has higher immune scores and vice versa (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). The relationship between both immune types and HLA gene sets demonstrated that the samples in the immune_H group had significantly higher expression of 12 genes (HLA-DMA, HLA-DOA, HLA-DMB, HLA-DPB1, HLA-DPA1, HLA-DPB2, HLA-DQA2, HLA-DQB2, HLA-DQA1, HLA-DRB5, HLA-DRA, HLA-DRB6) and 6 significantly lower expression of genes (HLA-A, HLA-C, HLA-E, HLA-B, HLA-L and HLA-F) compared with the immune_L group (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Furthermore, the immune_L group had more plasma cells and naive CD4\u003csup\u003e+\u003c/sup\u003e T cells, while the immune_H group had more regulatory T cells (Treg), memory B cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, neutrophils and resting NK cells (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). Total 112 immune-related DEGs between both groups were obtained (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE and \u003cstrong\u003eFigure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). WGCNA revealed that the associations between the module eigengenes and clinical characteristics were not significant (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF). In addition, GSEA analysis of the immune-associated genes showed that SLE was negatively correlated with propanoate metabolism, and valine leucine and isoleucine degradation (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG). There are several studies demonstrated that the possible etiological mechanisms of SLE and COVID-19 include immunometabolism dysfunction [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. Our results provide evidence that metabolites may be involved in the development of SLE, which might provide novel insights into investigating causal role of blood metabolites in development of SLE through a comprehensive genetic pathway.\u003c/p\u003e\n \u003cp\u003eTo identify immune-related DEGs that contribute to SLE and COVID-19 pathogenesis, we analyzed the six cohorts to identify immune-related DEGs with significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between the immune_H group and immune_L group (Figure S2). We selected and identified 4 upregulated immune-related differential gene expression (DEGs) (|fold change|\u0026gt;2, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) that were significantly related to SLE and COVID-19 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eH), among which C-X-C motif chemokine ligand 10 (CXCL10) showed a higher level of expression in patients of both groups. The CXCL10 was increased in various autoimmune diseases like SLE [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e], which may also be a key regulator of the \u0026apos;cytokine storm\u0026apos; immune response to SARS-CoV-2 infection [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. The common immune-related DEGs (CD3-TCR complex (CD3D), CXCL10, SH2 domain-containing 1A (SH2D1A), SH2 domain-containing 1B (SH2D1B)) of the six cohorts were obtained by the expression comparison of the DEGs in both groups with different immune status (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eH and \u003cstrong\u003eFigure S2\u003c/strong\u003e). All of these genes were upregulated in patients with COVID-19 and SLE (\u003cstrong\u003eTable S4\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Immune-related molecular subtype and pathway characteristics of SLE and COVID-19 patients\u003c/h2\u003e\n \u003cp\u003eWe further analyzed the immune-related molecular subtype and pathway characteristics of the SLE and COVID-19 patients. The GSE157103, GSE161731 and GSE163151 cohorts were enrolled into one COVID-19 cohort. Based on the gene expression status, three subtypes were obtained by unsupervised clustering (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). The proportion of males was a clear different between subgroups I and II (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). The proportion of age was also significantly distinction between subgroups I and II (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Subsequently, the relationship heatmap between eigengenes and clinical characteristics (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD) and the module eigengene dendrogram (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE) were obtained. Moreover, Gene Ontology (GO) and KEGG analyses were conducted to indicate the biological behavior among the three subgroups (\u003cstrong\u003eFigure S3\u003c/strong\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF). The different pathways associated with COVID-19 included the spliceosome, ferroptosis, tryptophan metabolism, T-cell receptor signaling, coronavirus disease-COVID-19, ribosome, malaria, EGFR tyrosine kinase inhibitor resistance, insulin signaling and cell cycle (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF). The GSE72509, GSE49454 and GSE110169 cohorts were enrolled into one SLE cohort for comparison with the COVID-19 cohorts (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eG). There was no difference in the proportion of females between subgroups I and II (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eH). The associations between eigengenes and clinical characteristics are shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eI. The GO and KEGG pathways associated with SLE included coronavirus disease-COVID-19, T-cell receptor signaling, ribosome, graft-versus-host disease, ECM-receptor interaction, nitrogen metabolism and spliceosome (\u003cstrong\u003eFigure S4\u003c/strong\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eJ). Therefore, the different pathways associated with both SLE and COVID-19 included coronavirus disease-COVID-19, T-cell receptor signaling, ribosome, graft-versus-host disease, extracellular matrix (ECM)-receptor interaction and spliceosome.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Comparison of the single-cell immune transcription atlas of PBMCs between SLE and COVID-19 patients\u003c/h2\u003e\n \u003cp\u003eTo explore the transcriptional features of PBMCs from SLE and COVID-19 patients at the single-cell level, we characterized the cell and molecular mechanisms of specific disease processes. ScRNA-seq was used to identify specific immune cell subsets of COVID-19 and SLE patients. After stringent quality control and filtering by multiple criteria. Transcriptomes of 25,059 and 23,902 single cells from COVID-19 and SLE samples belonging to four single-cell datasets, namely, GSE135779 (PBMCs, SLE), GSE142016 (PBMCs, SLE), GSE155222 (PBMCs, COVID-19) and GSE166992 (PBMCs, COVID-19), were obtained, with about 17,474 and 16,559 genes per cell detected, respectively. Then, the single-cell datasets were combined for further systematic comparison between SLE and COVID-19 samples. Based on PCA, the similar expression patterns of cells were cluster by using the UMAP algorithm. The analysis results further classified the two group samples into 21 clusters (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). We then performed a comparative study of the composition of PBMC subpopulations between the SLE and COVID-19 samples, we found that some cell clusters (including cluster 2, unknown 1 cells; cluster 11, unknown 2 cells; cluster 17, unknown 4 cells; cluster 14, platelet cells) were decreased in SLE patients (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB) and that some cell subpopulations (cluster 0, CD14\u003csup\u003e+\u003c/sup\u003e mono1 cells; cluster 9, CD8\u003csup\u003e+\u003c/sup\u003e naive T cells; cluster 8, CD4\u003csup\u003e+\u003c/sup\u003e memory T cells; cluster 3, CD8\u003csup\u003e+\u003c/sup\u003e effector T cells; cluster 6, CD8\u003csup\u003e+\u003c/sup\u003e T cells; cluster 10, CD14\u003csup\u003e+\u003c/sup\u003e monocyte 2 cells) were increased in SLE samples (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e. Considering the high dropout rate of scRNA-seq data, we here selected the dropClust, which is a novel algorithm for clustering and visualization of ultra-large single cell RNA-seq (scRNA-seq) data [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. We classified main cells sub-populations based on various cell markers. Finally, 16 cell clusters were identified, and other 5 cell clusters were \u0026ldquo;unknown\u0026rdquo; cell clusters (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC-F). The cell type-specific gene expressions were shown in the dot plot \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cstrong\u003e)\u003c/strong\u003e and the UMAP plot for cell clustering visualization (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). No specified unique cell subset in either group was detected based on the expression level of gene markers (refer to the database of CellMarker) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE). The top 3 DEGs of each subset are listed in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF. CD14\u003csup\u003e+\u003c/sup\u003e mono1 cells, naive T cells, unknown 1 cells, CD8\u003csup\u003e+\u003c/sup\u003e effector T cells and CD4\u003csup\u003e+\u003c/sup\u003e T cells were the most abundant cell types (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG). For the SLE patients, the dominant cell clusters were CD14\u003csup\u003e+\u003c/sup\u003e monocytes, naive T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, CD8\u003csup\u003e+\u003c/sup\u003e effector T cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, and CD4\u003csup\u003e+\u003c/sup\u003e memory T cells. However, in COVID-19 patients, unknown 1 cells, naive T cells, naive B1 cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, unknown 2 cells, and naive CD4\u003csup\u003e+\u003c/sup\u003e T cells were the top 6 dominant cell clusters.\u003c/p\u003e\n \u003cp\u003eThe transcriptomic features of each cluster were displayed based on gene expression levels across all PBMCs in the COVID-19 and SLE samples (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). The cell populations of the SLE and COVID-19 samples were also enriched in expressing CD3D, granulysin (GNLY) and CD79a molecule (CD79A) in the dot plot (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). As the unifying marker in COVID-19 patients, the ATP synthase (ATP5E) gene was downregulated while receptor for activated protein C kinase 1 (RACK1) gene was upregulated compared with SLE patients \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC-D\u003cstrong\u003e)\u003c/strong\u003e. We also found several differentially expressed genes, including RACK1 and ATP synthase F1 subunit epsilon (ATP5F1E), in SLE patients compared with COVID-19 patients from various cell clusters (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE-J and \u003cstrong\u003eFigure S5\u003c/strong\u003e). The results suggested a significant difference in the B-cell region in the two diseases. Therefore, we selected the B-cell datasets of the two diseases to further explore the underlying differences in characteristics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Differential immune characteristics of B-cell subpopulations in SLE and COVID-19 patients\u003c/h2\u003e\n \u003cp\u003eTo compare the immune characteristics of B-cell subpopulations between SLE and COVID-19 samples, the recently published B-cell single-cell datasets (GSE163121 (B cells, SLE), GSE164379 (B cells, COVID-19)) were merged for further systematic comparison between SLE and COVID-19 samples. Transcriptomes of 14,685 and 13060 single B cells from the COVID-19 and SLE patients were obtained, with 15,763 and 14,590 genes per cell detected, respectively. The analysis results classified the two group samples into 15 clusters using the UMAP plot (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Compared to COVID-19, the results showed that immune-related B-cell clusters, such as cluster 4, MT\u003csup\u003e+\u003c/sup\u003e B cells; cluster 6, CD83\u003csup\u003e+\u003c/sup\u003e B cells; and cluster 12, STAT4\u003csup\u003e+\u003c/sup\u003e B cells, were decreased (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). These B-cell subpopulations (cluster 1, CXCR5\u003csup\u003e+\u003c/sup\u003e B cells; cluster 5, HLA-DRB5\u003csup\u003e+\u003c/sup\u003e B cells) in the SLE samples were increased (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, B\u003cstrong\u003e).\u003c/strong\u003e Among the B cell subpopulations, TCL1A\u003csup\u003e+\u003c/sup\u003e naive B1 cells, CXCR5\u003csup\u003e+\u003c/sup\u003e B cells, CD80\u003csup\u003e+\u003c/sup\u003e B cells, TCL1A\u003csup\u003e+\u003c/sup\u003e naive B2 cells, MT\u003csup\u003e+\u003c/sup\u003e B cells, HLA-DRB5\u003csup\u003e+\u003c/sup\u003e B cells, CD83\u003csup\u003e+\u003c/sup\u003e B cells and CCL5\u003csup\u003e+\u003c/sup\u003e B cells were the most abundant B-cell types (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e. For the SLE patients, the dominant cell clusters were CXCR5\u003csup\u003e+\u003c/sup\u003e B and HLA-DRB5\u003csup\u003e+\u003c/sup\u003e B. However, in the COVID-19 patients, MT\u003csup\u003e+\u003c/sup\u003e B cells, CD83\u003csup\u003e+\u003c/sup\u003e B cells, CCL5\u003csup\u003e+\u003c/sup\u003e B cells, and STAT4\u003csup\u003e+\u003c/sup\u003e B cells were the top 4 dominant cell clusters. Based on the expression of main gene markers (refer to the Database of CellMarker), we identified 15 cell subsets, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC-E. The dot plot shows the expression levels of marker genes of well-known cell type \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC\u003cstrong\u003e)\u003c/strong\u003e. There was no specified unique cell subset in either group was detected based on the expression level of gene markers (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD), the top 3 DEGs of each subset are listed in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE.\u003c/p\u003e\n \u003cp\u003eThe cell populations of the SLE and COVID-19 samples were both enriched in the expression of the marker genes CD79A, IL7R and membrane spanning 4-domains A1(MS4A1) (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). As a unifying marker in SLE patients, there was higher expression of AC090498.1 and lower expression of RACK1 compared to COVID-19 patients \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB, C\u003cstrong\u003e)\u003c/strong\u003e. We found several differentially expressed genes, including RACK1, ATP5F1E, ATP5E and AC090498.1, in the SLE samples compared with the COVID-19 samples from various B-cell clusters (Figs. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD-M).\u003c/p\u003e\n \u003cp\u003eTo investigate the role of B cells in SLE and COVID-19, the pseudotime methods were used to simulate the differentiation trajectory of B cells. We divided the samples into multiple cell populations (states) under differentiation states according to the gene expression status, and generate an intuitive lineage development tree diagram to predict the differentiation and development track of cells. A total of 11 cell types and 12 states were identified in the SLE samples \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e, and 15 cell types and 5 states were subsequently identified in the COVID-19 samples \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e. Here, genes that were markedly differentially expressed along the pseudotime axis were further clustered into 11 modules according to their expression patterns in the SLE samples in a heatmap (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eC\u003cstrong\u003e)\u003c/strong\u003e. Previous findings suggested that cluster 2 was mainly composed of CD83\u003csup\u003e+\u003c/sup\u003e B cells, we thus used the branch of state 9 as the starting point \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eD\u003cstrong\u003e)\u003c/strong\u003e. For COVID-19, the genes that were markedly differentially expressed along the pseudotime axis were further clustered into 15 modules according to the expression patterns (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eE). State 5 contained more B cells from the COVID-19 group compared with the SLE group \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eF\u003cstrong\u003e)\u003c/strong\u003e. Previous findings indicated that cluster 5 was primarily composed of S100A10\u003csup\u003e+\u003c/sup\u003e B cells, we that used the branch of state 5 as the starting point \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eF\u003cstrong\u003e)\u003c/strong\u003e. Then, along the pseudotime axis, the expression of encoding genes was examined in the SLE and COVID-19 groups \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eG, H\u003cstrong\u003e)\u003c/strong\u003e. In our study, the comparisons of B cells by single-cell RNAseq provided evidence that B cells in diverse infectious diseases and in systemic autoimmune diseases are highly related and conducive to exploration share common drivers of differentiation and expansion.\u003c/p\u003e\n \u003cp\u003eThe above results suggested that the B cells expanded in different directions in the SLE and COVID-19 groups and that the B-cell clusters may drive different heterogeneity and cell state transitions in COVID-19 and SLE. To investigate this hypothesis, we applied CellChat functionalities to B-cell scRNA-seq datasets of COVID-19 and SLE. CellChat identifies communication patterns and predicts functions [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our results showed that B-cell clusters may communicate with other B-cell clusters via the MIF signaling pathway, thereby regulating cell function (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA-B). Moreover, the MIF signaling pathway networks (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eC-D) showed not only high signaling redundancy but also high target promiscuity (for instance, B-cell clusters can act as MIF targets). Cells have unique communication modes, including senders, mediators, receivers and influencers. Complex signaling networks were dissected by explicitly assigning sender and receiver cells to distinguish the MIF signaling pathway network. It demonstrated that the MIF signaling pathway network in the COVID-19 and SLE patients is of high redundancy, and multiple ligand sources can target most of B-cell clusters (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eE-F). Further analysis of ligand‒receptor demonstrated that immune signaling axes (MIF-CD74\u0026thinsp;+\u0026thinsp;CXCR4 and MIF-CD74\u0026thinsp;+\u0026thinsp;CD44) might participate in the intercellular crosstalk between B-cell subsets (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eG-H). Together, these results provide an opportunity to deeply probe underlying B-cell‒cell communication with multiple ligand‒receptor pairs (MIF-CD74\u0026thinsp;+\u0026thinsp;CXCR4, MIF-CD74\u0026thinsp;+\u0026thinsp;CD44) that are often deemed to drive heterogeneity and cell state transitions in COVID-19 and SLE.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe COVID-19 pandemic has become a major concern worldwide [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Patients with SLE, an autoimmune disease, are susceptible to COVID-19, which has attracted much attention in the context of the current pandemic. There are many similarities and differences between COVID-19 and SLE. SLE patients are at high risk of becoming infected with the coronavirus. Further examination of the molecular mechanisms in COVID-19 and SLE patients may contribute to finding better therapeutic options. Therefore, we systematically conducted a comparative analysis of SLE and COVID-19 from multiple perspectives.\u003c/p\u003e \u003cp\u003eFirst, we focused on the molecular characterization of the two diseases as it relates to the immune system, and we identified the common upregulated immune-related genes CD3D, CXCL10, SH2 domain containing 1B (SH2D1B)) by comparing two immunity clusters. Among the above immune-related gene regulators, CXCL10 is a proinflammatory chemokine that is involved in COVID-19 that leads to acute respiratory distress syndrome (ARDS) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. CXCL10 could be a key candidate gene related to the cytokine storm of COVID-19 ARDS patients [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. One study has shown that CXCL10 is upregulated in blood samples of COVID-19 patients [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. It is expected to become a therapeutic target [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. A previous study also demonstrated that CXCL10 is upregulated in PBMCs and B lymphocytes, serum and/or tissue of patients with SLE [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Moreover, CXCL10 responds to IFN-γ pathway inhibition [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Therefore, we speculated that targeting CXCL10 may be a promising strategy for treating SLE patients who also have COVID-19.\u003c/p\u003e \u003cp\u003eCOVID-19 vaccines are urgently needed to control the pandemic, for patients with SLE, scientific vaccination helps to reduce the risk of infection in order to achieve an adequate and durable immune response [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The association study about the association of HLA gene sets with immune status between SLE and COVID-19 could help screen potential targets for the immune system and explore the impact of the vaccine on protective immunity. Individual HLA alleles can influence the risk and the severity of viral infections. For COVID-19, these analyses may contribute to assessing the susceptibility of SARS-CoV-2 and the epidemiological level [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Clarification of these specific differences in HLA subtype between different groups may contribute to predicting the differential susceptibility or resistance to SARS-CoV-2 infections, especially for SLE patients.\u003c/p\u003e \u003cp\u003eConsidering that gene regulation is a complicated multisystem adjustment process, we also studied the integrated role of genes in the molecular subtypes of COVID-19 and SLE. Our study demonstrated that the signaling pathways of coronavirus disease-COVID-19, T-cell receptor signaling, ribosome, graft-versus-host disease, ECM-receptor interaction, and spliceosome were significantly activated in COVID-19 and SLE molecular subtypes. Significantly distinct pathway characteristics were observed in the different molecular subtypes, which also confirmed that the specific regulation pathways were remarkably associated with COVID-19 and SLE. Our results may contribute to facilitating the study of the relationships between molecular subtypes and especially pathways among COVID-19 and SLE.\u003c/p\u003e \u003cp\u003ePrevious studies have revealed that SLE with high heterogeneity and complexity features [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] is very challenging regarding both precision diagnosis and treatment when SLE patients also have COVID-19. According to these findings, the examination of immune cell dysfunction in COVID-19 and SLE patients may help to discover new pathogenesis mechanisms. It is also important for the clinical management of SLE and COVID-19 patients. Therefore, it is urgent to reveal the underlying molecular mechanism by high cellular resolution strategy in SLE and COVID-19 patients.\u003c/p\u003e \u003cp\u003eHerein, scRNA-seq methods were used to identify the molecular signature and cellular features to dissect their role in these two diseases at the single-cell level. There are 21 and 15 predominant subpopulations of cells with UMAP clustering were identified in the PBMCs and B cells of COVID-19 and SLE patients, respectively. Notably, the features of the cells with distinct transcriptomic patterns were recognized. For the PBMCs of SLE patients, the dominant cell clusters were CD14\u003csup\u003e+\u003c/sup\u003e monocytes, CD8\u003csup\u003e+\u003c/sup\u003e effector T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, naive T cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, and CD4\u003csup\u003e+\u003c/sup\u003e memory T cells. However, for COVID-19 patients, unknown 1 cells, naive T cells, naive B1 cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, unknown 2 cells, and naive CD4\u003csup\u003e+\u003c/sup\u003e T cells were the top 6 dominant cell clusters. Moreover, the results suggested that the significant difference in B cells restored the pathogenesis of the two diseases. Thus, we selected the B-cell datasets of the two diseases to further explore the underlying differences in characteristics. Based on comparative analysis, for the SLE patients, the dominant cell clusters were CXCR5\u003csup\u003e+\u003c/sup\u003e B and HLA-DRB5\u003csup\u003e+\u003c/sup\u003e B cells. However, in the COVID-19 patients, MT\u003csup\u003e+\u003c/sup\u003e B cells, CD83\u003csup\u003e+\u003c/sup\u003e B cells, CCL5\u003csup\u003e+\u003c/sup\u003e B cells, and STAT4\u003csup\u003e+\u003c/sup\u003e B cells were the top 4 dominant cell clusters. In general, our results demonstrated that differences in the B-cell subpopulations existed between the two groups. The differences in the B-cell subpopulations existed between the two groups showed that B-cell clusters might drive cell state heterogeneity and transitions in COVID-19 and SLE. B-cell clusters in diverse infectious diseases and in systemic autoimmune diseases are highly related, which share respective drivers of differentiation and expansion. However, B-cell clusters in different diseases are not identical and even show discrete disease-specific features. As we know, B cells can recognize the antigen (foreign body) and produce antibodies against it. B cells involves in humoral-mediated immunity or antibody-mediated immunity (AMI). They fight and protect the body from the virus that enters the bloodstream [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. A better understanding of the commonality and differences in the B cell responses in these two diseases may provide critical insights into the development of vaccines that drive pathogen-specific antibody responses and avoid autoimmunity.\u003c/p\u003e \u003cp\u003eIt is well established that B cells are one of the main immune cells with abnormal differentiation in SLE patients [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Analyzing the transcription atlas of relevant B-cell subsets in SLE patients would facilitate new ideals to targeting pathogenic B cells. In our study, pseudotime trajectory analyses indicated that B cells may drive heterogeneity and cell state transitions in COVID-19 and SLE. A total of 11 cell types and 12 states were identified in the SLE group, and a total of 15 cell types and 5 states were subsequently identified in the COVID-19 group. Based on our findings that cluster 2 primarily consisted of CD83\u003csup\u003e+\u003c/sup\u003e B cells, the branch of state 9 was the starting point. State 5 comprised more S100A10\u003csup\u003e+\u003c/sup\u003e B cells from the COVID-19 group compared with the SLE group; thus, the branch of state 5 was set as the starting point. These results suggested that B cells in the SLE and COVID-19 groups were expanded in different directions. Moreover, different intimate cell‒cell communications among B-cell clusters were identified in the two diseases. We also found that the high expression of receptor‒ligand complexes, such as MIF-CD74\u0026thinsp;+\u0026thinsp;CXCR4 and MIF-CD74\u0026thinsp;+\u0026thinsp;CD44, may play crucial roles in SLE and COVID-19 patients. The activation of MIF-related pathways is participated in the formation of B-cell communication in the two diseases. Our findings provide novel insight to support that MIF-related signaling pathways might potentially serve as an underlying molecular mechanism of B cells in SLE and COVID-19. Thus, this finding suggests that the B-cell populations could be the candidate targets for MIF inhibitors. In addition, we also discovered several unidentified differentially expressed genes, such as RACK1, ATP5F1E and AC090498.1, in the SLE samples compared to the COVID-19 samples from various fine-sorted B-cell clusters. The differential expression of the key genes we identified in PBMCs and B cells might facilitate our understanding of SLE and COVID-19. Through database analysis, our results showed that these genes that are related to immune cells and involved in the regulation of immune cell activity. For instances, RACK1 was identified as a novel host factor required for Zika virus (ZIKV) replication. The experiments of depletion of RACK1 demonstrated that RACK1 is important for replication of SARS-CoV-2 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. A better understanding of the commonality and differences about these key genes in the PBMC and B cell in these two diseases might provide critical insights into the development of vaccines that drive pathogen-specific antibody responses and avoid autoimmunity.\u003c/p\u003e \u003cp\u003eA recent study reported that Omicron RBD memory B cell recognition was substantially reduced to 42% compared with other variants in subjects 6 months post-vaccination [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Understanding the SARS-CoV-2 RBD-specific naive repertoire may inform potential responses capable of recognizing future SARS-CoV-2 variants or emerging coronaviruses, enabling the development of pan-coronavirus vaccines aimed at engaging protective germline response [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Memory B cell reserves can generate protective antibodies against repeated SARS-CoV-2 infections, but with unknown reach from original infection to antigenically drifted variants. Although emerging SARS-CoV-2 variants of concern escape binding by many members of the groups associated with the most potent neutralizing activity, some antibodies in each of those groups retain affinity, suggesting that otherwise redundant components of a primary immune response are important for durable protection from evolving pathogens [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Scheid JF et al found that the SARS-CoV-2-specific B cell repertoire consists of transcriptionally distinct B cell populations with cells producing potently neutralizing antibodies (nAbs) localized in two clusters that resemble memory and activated B cells [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Together, our results characterize transcriptional differences among SARS-CoV-2-specific and SLE B cells and would provide reference for immunogen and therapeutic design against coronaviruses.\u003c/p\u003e \u003cp\u003eSeveral limitations are existed in our study, and more comprehensive studies are needed. First, we used data from different studies based on the GEO database, which might cause unavoidable heterogeneity. Second, the exact molecular mechanisms of the genetic signatures need to be further identified by experiments and clinical practice in the future. Further explores are urged to confirm the functions of these DEGs in SLE and COVID-19. Our primary objective was to discuss the multifaceted impacts on the COVID-19 pandemic for SLE patients in transcriptomic and single-cell analyses. These theoretical perspectives lay a foundation for designing specific and effective therapeutic methods for fighting SARS-CoV-2.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, we analyzed the difference in COVID-19 and SLE from multiple dimensions. The systematic evaluation of characteristic differences between different subgroups in our study makes sense for the heterogeneity and treatment complexity of the two diseases. Genetic testing of the key immune gene CXCL10 and molecular characteristics might help promote the diagnosis and treatment of COVID-19 and SLE patients. Immune cell disorder appears in COVID-19 and SLE patients at the single-cell level and in blood tests. The significantly upregulated and downregulated genes in PBMCs and B-cell clusters of SLE and COVID-19 patients mainly included RACK1, ATP5E and AC090498.1. Moreover, we provided an opportunity to comprehensively probe underlying B-cell‒cell communication with multiple ligand‒receptor pairs (MIF-CD74\u0026thinsp;+\u0026thinsp;CD44, MIF-CD74\u0026thinsp;+\u0026thinsp;CXCR4) and the differentiation trajectory of B-cell clusters that can drive cell state heterogeneity and transitions in COVID-19 and SLE. Therefore, this study revealed the cellular and molecular associations of COVID-19 with SLE, especially for the synergistic complexities of COVID-19 and SLE, which might provide clues for personalized immunotherapy for SLE patients infected with COVID-19 in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ecoronavirus disease 2019 (COVID-19); systemic lupus erythematosus (SLE); peripheral blood mononuclear cells (PBMCs); severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); angiotensin converting enzyme 2 (ACE2); human leukocyte antigens (HLA); Single-cell RNA sequencing (scRNA-seq); single-sample gene set enrichment (ssGSEA); Gene set variation analysis (GSVA); weighted gene coexpression network analysis (WGCNA); principal component analysis (PCA); Molecular Signatures Database (MSigDB); Kyoto Encyclopedia of Genes and Genomes (KEGG); Uniform Manifold Approximation and Projection (UMAP); Gene set enrichment analysis (GSEA); Single-sample GSEA (ssGSEA); differential gene expression (DEGs); regulatory T cells (Treg); CD3-TCR complex (CD3D); SH2 domain-containing 1A (SH2D1A); SH2 domain-containing 1B (SH2D1B); C-X-C motif chemokine ligand 10 (CXCL10); Gene Ontology (GO); extracellular matrix (ECM); SH2 domain containing 1B (SH2D1B); granulysin (GNLY); acute respiratory distress syndrome (ARDS); antibody-mediated immunity (AMI); receptor for activated protein C kinase 1 (RACK1); Zika virus (ZIKV); membrane spanning 4-domains A1(MS4A1); CD79a molecule (CD79A) ; ATP synthase (ATP5E) ; ATP synthase F1 subunit epsilon (ATP5F1E).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenerated Statement: All analyses were performed based on publicly available datasets in this study. These datasets are available in the GEO database. The accession number(s) of datasets are included in the article. The information of supporting the findings of this study are publicly available through the supplementary materials, and other reasonable requests are available from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll listed authors contribute substantially to this work and approved its publication.\u0026nbsp;R.D. and Z.R. designed the study. X.Z. performed\u0026nbsp;the\u0026nbsp;data analysis and experimental validation. M.Z., Y.J., W.L., S.L. and C.G. conducted the screening of the public datasets. L.Z. and B.N. managed all the raw data. R.D. and W.L. edited the manuscript. Project funding was provided by X.Z. and R.D.. All authors have read and agreed to the publication of the work in this manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest in this work.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Natural Science Foundation of Chongqing (cstc2021jcyj-msxmX0058 and CSTB2022NSCQ-MSX1428). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the colleagues for useful discussions and comments.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDoaty S, Agrawal H, Bauer E, Furst DE. Infection and Lupus: Which Causes Which? Curr Rheumatol Rep. 2016; 18(3): 13.https://doi.org/10.1007/s11926-016-0561-4.\u003c/li\u003e\n\u003cli\u003eSawalha AH, Zhao M, Coit P, Lu Q. Epigenetic dysregulation of ACE2 and interferon-regulated genes might suggest increased COVID-19 susceptibility and severity in lupus patients\u003cem\u003e.\u003c/em\u003e medRxiv. 2020.https://doi.org/10.1101/2020.03.30.20047852.\u003c/li\u003e\n\u003cli\u003eThanou A, Sawalha AH. SARS-CoV-2 and Systemic Lupus Erythematosus\u003cem\u003e.\u003c/em\u003e Curr Rheumatol Rep. 2021; 23(2): 8.https://doi.org/10.1007/s11926-020-00973-w.\u003c/li\u003e\n\u003cli\u003eTiendrebeogo WJS, Kabore F, Diendere EA, Ouedraogo DD. Case Series of Chronic Inflammatory Rheumatic Disease Patients Infected by Coronavirus Disease 2019 (COVID-19)\u003cem\u003e.\u003c/em\u003e Case Rep Rheumatol. 2020; 2020: 8860492.https://doi.org/10.1155/2020/8860492.\u003c/li\u003e\n\u003cli\u003eZacharias H, Dubey S, Koduri G, D\u0026apos;Cruz D. Rheumatological complications of Covid 19\u003cem\u003e.\u003c/em\u003e Autoimmun Rev. 2021; 20(9): 102883.https://doi.org/10.1016/j.autrev.2021.102883.\u003c/li\u003e\n\u003cli\u003eLiu Y, Sawalha AH, Lu Q. COVID-19 and autoimmune diseases\u003cem\u003e.\u003c/em\u003e Curr Opin Rheumatol. 2021; 33(2): 155-162.https://doi.org/10.1097/BOR.0000000000000776.\u003c/li\u003e\n\u003cli\u003eHalpert G, Shoenfeld Y. SARS-CoV-2, the autoimmune virus\u003cem\u003e.\u003c/em\u003e Autoimmun Rev. 2020; 19(12): 102695.https://doi.org/10.1016/j.autrev.2020.102695.\u003c/li\u003e\n\u003cli\u003eShayestehpour M, Zamani B. Systemic lupus erythematosus and varicella-like rash following COVID-19 in a previously healthy patient\u003cem\u003e.\u003c/em\u003e J Med Virol. 2021; 93(5): 2599.https://doi.org/10.1002/jmv.26838.\u003c/li\u003e\n\u003cli\u003eSpihlman AP, Gadi N, Wu SC, Moulton VR. COVID-19 and Systemic Lupus Erythematosus: Focus on Immune Response and Therapeutics\u003cem\u003e.\u003c/em\u003e Front Immunol. 2020; 11: 589474.https://doi.org/10.3389/fimmu.2020.589474.\u003c/li\u003e\n\u003cli\u003eGralinski LE, Menachery VD. Return of the Coronavirus: 2019-nCoV\u003cem\u003e.\u003c/em\u003e Viruses. 2020; 12(2).https://doi.org/10.3390/v12020135.\u003c/li\u003e\n\u003cli\u003eMisra DP, Agarwal V, Gasparyan AY, Zimba O. Rheumatologists\u0026apos; perspective on coronavirus disease 19 (COVID-19) and potential therapeutic targets\u003cem\u003e.\u003c/em\u003e Clin Rheumatol. 2020; 39(7): 2055-2062.https://doi.org/10.1007/s10067-020-05073-9.\u003c/li\u003e\n\u003cli\u003eKatz-Agranov N, Zandman-Goddard G. Autoimmunity and COVID-19 - The microbiotal connection\u003cem\u003e.\u003c/em\u003e Autoimmun Rev. 2021; 20(8): 102865.https://doi.org/10.1016/j.autrev.2021.102865.\u003c/li\u003e\n\u003cli\u003eGkogkou E, Barnasas G, Vougas K, Trougakos IP. Expression profiling meta-analysis of ACE2 and TMPRSS2, the putative anti-inflammatory receptor and priming protease of SARS-CoV-2 in human cells, and identification of putative modulators\u003cem\u003e.\u003c/em\u003e Redox Biol. 2020; 36: 101615.https://doi.org/10.1016/j.redox.2020.101615.\u003c/li\u003e\n\u003cli\u003eZhou SY, Zhang C, Shu WJ, Chong LY, He J, Xu Z, et al. Emerging Roles of Coronavirus in Autoimmune Diseases\u003cem\u003e.\u003c/em\u003e Arch Med Res. 2021; 52(7): 665-672.https://doi.org/10.1016/j.arcmed.2021.03.012.\u003c/li\u003e\n\u003cli\u003eDendrou CA, Petersen J, Rossjohn J, Fugger L. HLA variation and disease\u003cem\u003e.\u003c/em\u003e Nat Rev Immunol. 2018; 18(5): 325-339.https://doi.org/10.1038/nri.2017.143.\u003c/li\u003e\n\u003cli\u003eMcHugh J. New evidence for the \u0026apos;cusp theory\u0026apos; to explain HLA associations in SLE\u003cem\u003e.\u003c/em\u003e Nat Rev Rheumatol. 2022; 18(10): 552.https://doi.org/10.1038/s41584-022-00835-y.\u003c/li\u003e\n\u003cli\u003eDeb P, Zannat KE, Talukder S, Bhuiyan AH, Jilani MSA, Saif-Ur-Rahman KM. Association of HLA gene polymorphism with susceptibility, severity, and mortality of COVID-19: A systematic review\u003cem\u003e.\u003c/em\u003e HLA. 2022; 99(4): 281-312.https://doi.org/10.1111/tan.14560.\u003c/li\u003e\n\u003cli\u003eLangton DJ, Bourke SC, Lie BA, Reiff G, Natu S, Darlay R, et al. The influence of HLA genotype on the severity of COVID-19 infection\u003cem\u003e.\u003c/em\u003e HLA. 2021; 98(1): 14-22.https://doi.org/10.1111/tan.14284.\u003c/li\u003e\n\u003cli\u003ePapalexi E, Satija R. Single-cell RNA sequencing to explore immune cell heterogeneity\u003cem\u003e.\u003c/em\u003e Nat Rev Immunol. 2018; 18(1): 35-45.https://doi.org/10.1038/nri.2017.76.\u003c/li\u003e\n\u003cli\u003eWang Y, Xie X, Zhang C, Su M, Gao S, Wang J, et al. Rheumatoid arthritis, systemic lupus erythematosus and primary Sjogren\u0026apos;s syndrome shared megakaryocyte expansion in peripheral blood\u003cem\u003e.\u003c/em\u003e Ann Rheum Dis. 2022; 81(3): 379-385.https://doi.org/10.1136/annrheumdis-2021-220066.\u003c/li\u003e\n\u003cli\u003eDer E, Suryawanshi H, Morozov P, Kustagi M, Goilav B, Ranabothu S, et al. Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways\u003cem\u003e.\u003c/em\u003e Nat Immunol. 2019; 20(7): 915-927.https://doi.org/10.1038/s41590-019-0386-1.\u003c/li\u003e\n\u003cli\u003eHe Y, Jiang Z, Chen C, Wang X. Classification of triple-negative breast cancers based on Immunogenomic profiling\u003cem\u003e.\u003c/em\u003e J Exp Clin Cancer Res. 2018; 37(1): 327.https://doi.org/10.1186/s13046-018-1002-1.\u003c/li\u003e\n\u003cli\u003eHanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data\u003cem\u003e.\u003c/em\u003e BMC Bioinformatics. 2013; 14: 7.https://doi.org/10.1186/1471-2105-14-7.\u003c/li\u003e\n\u003cli\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies\u003cem\u003e.\u003c/em\u003e Nucleic Acids Res. 2015; 43(7): e47.https://doi.org/10.1093/nar/gkv007.\u003c/li\u003e\n\u003cli\u003eBhattacharya S, Andorf S, Gomes L, Dunn P, Schaefer H, Pontius J, et al. ImmPort: disseminating data to the public for the future of immunology\u003cem\u003e.\u003c/em\u003e Immunol Res. 2014; 58(2-3): 234-239.https://doi.org/10.1007/s12026-014-8516-1.\u003c/li\u003e\n\u003cli\u003eLangfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis\u003cem\u003e.\u003c/em\u003e BMC Bioinformatics. 2008; 9: 559.https://doi.org/10.1186/1471-2105-9-559.\u003c/li\u003e\n\u003cli\u003eLiberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0\u003cem\u003e.\u003c/em\u003e Bioinformatics. 2011; 27(12): 1739-1740.https://doi.org/10.1093/bioinformatics/btr260.\u003c/li\u003e\n\u003cli\u003eYu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters\u003cem\u003e.\u003c/em\u003e OMICS. 2012; 16(5): 284-287.https://doi.org/10.1089/omi.2011.0118.\u003c/li\u003e\n\u003cli\u003eStuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, 3rd, et al. Comprehensive Integration of Single-Cell Data\u003cem\u003e.\u003c/em\u003e Cell. 2019; 177(7): 1888-1902 e1821.https://doi.org/10.1016/j.cell.2019.05.031.\u003c/li\u003e\n\u003cli\u003eZhang X, Lan Y, Xu J, Quan F, Zhao E, Deng C, et al. CellMarker: a manually curated resource of cell markers in human and mouse\u003cem\u003e.\u003c/em\u003e Nucleic Acids Res. 2019; 47(D1): D721-D728.https://doi.org/10.1093/nar/gky900.\u003c/li\u003e\n\u003cli\u003eKuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update\u003cem\u003e.\u003c/em\u003e Nucleic Acids Res. 2016; 44(W1): W90-97.https://doi.org/10.1093/nar/gkw377.\u003c/li\u003e\n\u003cli\u003eJin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH, et al. Inference and analysis of cell-cell communication using CellChat\u003cem\u003e.\u003c/em\u003e Nat Commun. 2021; 12(1): 1088.https://doi.org/10.1038/s41467-021-21246-9.\u003c/li\u003e\n\u003cli\u003eEfremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes\u003cem\u003e.\u003c/em\u003e Nat Protoc. 2020; 15(4): 1484-1506.https://doi.org/10.1038/s41596-020-0292-x.\u003c/li\u003e\n\u003cli\u003eTrapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells\u003cem\u003e.\u003c/em\u003e Nat Biotechnol. 2014; 32(4): 381-386.https://doi.org/10.1038/nbt.2859.\u003c/li\u003e\n\u003cli\u003eQiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA, et al. Reversed graph embedding resolves complex single-cell trajectories\u003cem\u003e.\u003c/em\u003e Nat Methods. 2017; 14(10): 979-982.https://doi.org/10.1038/nmeth.4402.\u003c/li\u003e\n\u003cli\u003eRamalho R, Rao M, Zhang C, Agrati C, Ippolito G, Wang FS, et al. Immunometabolism: new insights and lessons from antigen-directed cellular immune responses\u003cem\u003e.\u003c/em\u003e Semin Immunopathol. 2020; 42(3): 279-313.https://doi.org/10.1007/s00281-020-00798-w.\u003c/li\u003e\n\u003cli\u003eLercher A, Baazim H, Bergthaler A. Systemic Immunometabolism: Challenges and Opportunities\u003cem\u003e.\u003c/em\u003e Immunity. 2020; 53(3): 496-509.https://doi.org/10.1016/j.immuni.2020.08.012.\u003c/li\u003e\n\u003cli\u003ePalsson-McDermott EM, O\u0026apos;Neill LAJ. Targeting immunometabolism as an anti-inflammatory strategy\u003cem\u003e.\u003c/em\u003e Cell Res. 2020; 30(4): 300-314.https://doi.org/10.1038/s41422-020-0291-z.\u003c/li\u003e\n\u003cli\u003eThomas R, Wang W, Su DM. Contributions of Age-Related Thymic Involution to Immunosenescence and Inflammaging\u003cem\u003e.\u003c/em\u003e Immun Ageing. 2020; 17: 2.https://doi.org/10.1186/s12979-020-0173-8.\u003c/li\u003e\n\u003cli\u003eOmarjee L, Perrot F, Meilhac O, Mahe G, Bousquet G, Janin A. Immunometabolism at the cornerstone of inflammaging, immunosenescence, and autoimmunity in COVID-19\u003cem\u003e.\u003c/em\u003e Aging (Albany NY). 2020; 12(24): 26263-26278.https://doi.org/10.18632/aging.202422.\u003c/li\u003e\n\u003cli\u003eLee EY, Lee ZH, Song YW. CXCL10 and autoimmune diseases\u003cem\u003e.\u003c/em\u003e Autoimmun Rev. 2009; 8(5): 379-383.https://doi.org/10.1016/j.autrev.2008.12.002.\u003c/li\u003e\n\u003cli\u003eZhang N, Zhao YD, Wang XM. CXCL10 an important chemokine associated with cytokine storm in COVID-19 infected patients\u003cem\u003e.\u003c/em\u003e Eur Rev Med Pharmacol Sci. 2020; 24(13): 7497-7505.https://doi.org/10.26355/eurrev_202007_21922.\u003c/li\u003e\n\u003cli\u003eSinha D, Kumar A, Kumar H, Bandyopadhyay S, Sengupta D. dropClust: efficient clustering of ultra-large scRNA-seq data\u003cem\u003e.\u003c/em\u003e Nucleic Acids Res. 2018; 46(6): e36.https://doi.org/10.1093/nar/gky007.\u003c/li\u003e\n\u003cli\u003eOliviero A, de Castro F, Coperchini F, Chiovato L, Rotondi M. COVID-19 Pulmonary and Olfactory Dysfunctions: Is the Chemokine CXCL10 the Common Denominator? Neuroscientist. 2021; 27(3): 214-221.https://doi.org/10.1177/1073858420939033.\u003c/li\u003e\n\u003cli\u003eBlot M, Jacquier M, Glele LA, Beltramo G, Nguyen M, Bonniaud P, et al. Correction to: CXCL10 could drive longer duration of mechanical ventilation during COVID-19 ARDS\u003cem\u003e.\u003c/em\u003e Crit Care. 2021; 25(1): 143.https://doi.org/10.1186/s13054-021-03559-9.\u003c/li\u003e\n\u003cli\u003eLi S, Duan X, Li Y, Li M, Gao Y, Li T, et al. Differentially expressed immune response genes in COVID-19 patients based on disease severity\u003cem\u003e.\u003c/em\u003e Aging (Albany NY). 2021; 13(7): 9265-9276.https://doi.org/10.18632/aging.202877.\u003c/li\u003e\n\u003cli\u003eZhang R, Li Y, Pan B, Li Y, Liu A, Li X. Increased expression of hub gene CXCL10 in peripheral blood mononuclear cells of patients with systemic lupus erythematosus\u003cem\u003e.\u003c/em\u003e Exp Ther Med. 2019; 18(5): 4067-4075.https://doi.org/10.3892/etm.2019.8013.\u003c/li\u003e\n\u003cli\u003eWelcher AA, Boedigheimer M, Kivitz AJ, Amoura Z, Buyon J, Rudinskaya A, et al. Blockade of interferon-gamma normalizes interferon-regulated gene expression and serum CXCL10 levels in patients with systemic lupus erythematosus\u003cem\u003e.\u003c/em\u003e Arthritis Rheumatol. 2015; 67(10): 2713-2722.https://doi.org/10.1002/art.39248.\u003c/li\u003e\n\u003cli\u003eTang W, Gartshteyn Y, Ricker E, Inzerillo S, Murray S, Khalili L, et al. The Use of COVID-19 Vaccines in Patients with SLE\u003cem\u003e.\u003c/em\u003e Curr Rheumatol Rep. 2021; 23(11): 79.https://doi.org/10.1007/s11926-021-01046-2.\u003c/li\u003e\n\u003cli\u003eTavasolian F, Rashidi M, Hatam GR, Jeddi M, Hosseini AZ, Mosawi SH, et al. HLA, Immune Response, and Susceptibility to COVID-19\u003cem\u003e.\u003c/em\u003e Front Immunol. 2020; 11: 601886.https://doi.org/10.3389/fimmu.2020.601886.\u003c/li\u003e\n\u003cli\u003eHuang X, Luu LDW, Jia N, Zhu J, Fu J, Xiao F, et al. Multi-Platform Omics Analysis Reveals Molecular Signatures for Pathogenesis and Activity of Systemic Lupus Erythematosus\u003cem\u003e.\u003c/em\u003e Front Immunol. 2022; 13: 833699.https://doi.org/10.3389/fimmu.2022.833699.\u003c/li\u003e\n\u003cli\u003eAmbegaonkar AA, Holla P, Dizon BL, Sohn H, Pierce SK. Atypical B cells in chronic infectious diseases and systemic autoimmunity: puzzles with many missing pieces\u003cem\u003e.\u003c/em\u003e Curr Opin Immunol. 2022; 77: 102227.https://doi.org/10.1016/j.coi.2022.102227.\u003c/li\u003e\n\u003cli\u003eDizon BLP, Pierce SK. The tangled web of autoreactive B cells in malaria immunity and autoimmune disease\u003cem\u003e.\u003c/em\u003e Trends Parasitol. 2022; 38(5): 379-389.https://doi.org/10.1016/j.pt.2022.01.005.\u003c/li\u003e\n\u003cli\u003eShue B, Chiramel AI, Cerikan B, To TH, Frolich S, Pederson SM, et al. Genome-Wide CRISPR Screen Identifies RACK1 as a Critical Host Factor for Flavivirus Replication\u003cem\u003e.\u003c/em\u003e J Virol. 2021; 95(24): e0059621.https://doi.org/10.1128/JVI.00596-21.\u003c/li\u003e\n\u003cli\u003eTarke A, Coelho CH, Zhang Z, Dan JM, Yu ED, Methot N, et al. SARS-CoV-2 vaccination induces immunological T cell memory able to cross-recognize variants from Alpha to Omicron\u003cem\u003e.\u003c/em\u003e Cell. 2022; 185(5): 847-859 e811.https://doi.org/10.1016/j.cell.2022.01.015.\u003c/li\u003e\n\u003cli\u003eFeldman J, Bals J, Altomare CG, St Denis K, Lam EC, Hauser BM, et al. Naive human B cells engage the receptor binding domain of SARS-CoV-2, variants of concern, and related sarbecoviruses\u003cem\u003e.\u003c/em\u003e Sci Immunol. 2021; 6(66): eabl5842.https://doi.org/10.1126/sciimmunol.abl5842.\u003c/li\u003e\n\u003cli\u003eTong P, Gautam A, Windsor IW, Travers M, Chen Y, Garcia N, et al. Memory B cell repertoire for recognition of evolving SARS-CoV-2 spike\u003cem\u003e.\u003c/em\u003e Cell. 2021; 184(19): 4969-4980 e4915.https://doi.org/10.1016/j.cell.2021.07.025.\u003c/li\u003e\n\u003cli\u003eScheid JF, Barnes CO, Eraslan B, Hudak A, Keeffe JR, Cosimi LA, et al. B cell genomics behind cross-neutralization of SARS-CoV-2 variants and SARS-CoV\u003cem\u003e.\u003c/em\u003e Cell. 2021; 184(12): 3205-3221 e3224.https://doi.org/10.1016/j.cell.2021.04.032.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"inflammation-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"inre","sideBox":"Learn more about [Inflammation Research](http://link.springer.com/journal/11)","snPcode":"11","submissionUrl":"https://submission.nature.com/new-submission/11/3","title":"Inflammation Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"COVID-19, Systemic lupus erythematosus, Single cell, RNA sequence, Immune cells","lastPublishedDoi":"10.21203/rs.3.rs-2932364/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2932364/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoronavirus disease 2019 (COVID-19) shares similar immune characteristics with autoimmune diseases like systemic lupus erythematosus (SLE). However, such associations have not yet been investigated at the single-cell level. Thus, in this study, we integrated and analyzed RNA sequencing results from different patients and normal controls from the GEO database and identified subsets of immune cells that might involve in the pathogenesis of SLE and COVID-19. We also disentangled the characteristic alterations in cell and molecular subset proportions as well as gene expression patterns in SLE patients compared with COVID-19 patients. Key immune characteristic genes (such as CXCL10 and RACK1) and multiple immune-related pathways (such as the coronavirus disease-COVID-19, T-cell receptor signaling, and MIF-related signaling pathways) were identified. We also highlighted the differences in peripheral blood mononuclear cells (PBMCs) between SLE and COVID-19 patients. Moreover, we provided an opportunity to comprehensively probe underlying B-cell‒cell communication with multiple ligand‒receptor pairs (MIF-CD74\u0026thinsp;+\u0026thinsp;CXCR4, MIF-CD74\u0026thinsp;+\u0026thinsp;CD44) and the differentiation trajectory of B-cell clusters that is deemed to promote cell state transitions in COVID-19 and SLE. Our results demonstrate the immune response differences and immune characteristic similarities, such as the cytokine storm, between COVID-19 and SLE, which might pivotally function in the pathogenesis of the two diseases and provide potential intervention targets for both diseases.\u003c/p\u003e","manuscriptTitle":"Featured immune characteristics of COVID-19 and systemic lupus erythematosus revealed by multidimensional integrated analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-18 12:45:49","doi":"10.21203/rs.3.rs-2932364/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-06-07T19:47:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-06-03T10:19:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0b7e7911-8dcf-4b33-85d6-0542a6aa256b","date":"2023-05-24T13:02:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-05-23T13:09:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-05-15T10:19:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-05-15T10:19:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Inflammation Research","date":"2023-05-14T02:32:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"inflammation-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"inre","sideBox":"Learn more about [Inflammation Research](http://link.springer.com/journal/11)","snPcode":"11","submissionUrl":"https://submission.nature.com/new-submission/11/3","title":"Inflammation Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d4a5b70d-ab04-4c7b-81af-d0367e578754","owner":[],"postedDate":"May 18th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-09-25T15:08:10+00:00","versionOfRecord":{"articleIdentity":"rs-2932364","link":"https://doi.org/10.1007/s00011-023-01791-3","journal":{"identity":"inflammation-research","isVorOnly":false,"title":"Inflammation Research"},"publishedOn":"2023-09-19 15:00:41","publishedOnDateReadable":"September 19th, 2023"},"versionCreatedAt":"2023-05-18 12:45:49","video":"","vorDoi":"10.1007/s00011-023-01791-3","vorDoiUrl":"https://doi.org/10.1007/s00011-023-01791-3","workflowStages":[]},"version":"v1","identity":"rs-2932364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2932364","identity":"rs-2932364","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}