Deciphering the molecular and cellular atlas of immune cells in septic patients with different bacterial infections.

BACKGROUND: Sepsis is a life-threatening organ dysfunction caused by abnormal immune responses to various, predominantly bacterial, infections. Different bacterial infections lead to substantial variation in disease manifestation and therapeutic strategies. However, the underlying cellular heterogeneity and mechanisms involved remain poorly understood. METHODS: Multiple bulk transcriptome datasets from septic patients with 12 types of bacterial infections were integrated to identify signature genes for each infection. Signature genes were mapped onto an integrated large single-cell RNA (scRNA) dataset from septic patients, to identify subsets of cells associated with different sepsis types, and multiple omics datasets were combined to reveal the underlying molecular mechanisms. In addition, an scRNA dataset and spatial transcriptome data were used to identify signaling pathways in sepsis-related cells. Finally, molecular screening, optimization, and de novo design were conducted to identify potential targeted drugs and compounds. RESULTS: We elucidated the cellular heterogeneity among septic patients with different bacterial infections. In Escherichia coli (E. coli) sepsis, 19 signature genes involved in epigenetic regulation and metabolism were identified, of which DRAM1 was demonstrated to promote autophagy and glycolysis in response to E. coli infection. DRAM1 upregulation was confirmed in an independent sepsis cohort. Further, we showed that DRAM1 could maintain survival of a pro-inflammatory monocyte subset, C10_ULK1, which induces systemic inflammation by interacting with other cell subsets via resistin and integrin signaling pathways in blood and kidney tissue, respectively. Finally, retapamulin was identified and optimized as a potential drug for treatment of E. coli sepsis targeting the signature gene, DRAM1, and inhibiting E. coli protein synthesis. Several other targeted drugs were also identified in other types of sepsis, including nystatin targeting C1QA in Neisseria sepsis and dalfopristin targeting CTSD in Streptococcus viridans sepsis. CONCLUSION: Our study provides a comprehensive overview of the cellular heterogeneity and underlying mechanisms in septic patients with various bacterial infections, providing insights to inform development of stratified targeted therapies for sepsis.

Identification and validation of key biomarkers based on RNA methylation genes in sepsis.

Background: RNA methylation is closely involved in immune regulation, but its role in sepsis remains unknown. Here, we aim to investigate the role of RNA methylation-associated genes (RMGs) in classifying and diagnosing of sepsis. Methods: Five types of RMGs (m1A, m5C, m6Am, m7G and Ψ) were used to identify sepsis subgroups based on gene expression profile data obtained from the GEO database (GSE57065, GSE65682, and GSE95233). Unsupervised clustering analysis was used to identify distinct RNA modification subtypes. The CIBERSORT, WGCNA, GO and KEGG analysis were performed to explore immune infiltration pattern and biological function of each cluster. RF, SVM, XGB, and GLM algorithm were applied to identify the diagnostic RMGs in sepsis. Finally, the expression levels of the five key RMGs were verified by collecting PBMCs from septic patients using qRT-PCR, and their diagnostic efficacy for sepsis was verified in combination with clinical data using ROC analysis. Results: Sepsis was divided into three subtypes (cluster 1 to 3). Cluster 1 highly expressed NSUN7 and TRMT6, with the characteristic of neutrophil activation and upregulation of MAPK signaling pathways. Cluster 2 highly expressed NSUN3, and was featured by the regulation of mRNA stability and amino acid metabolism. NSUN5 and NSUN6 were upregulated in cluster 3 which was involved in ribonucleoprotein complex biogenesis and carbohydrate metabolism pathways. In addition, we identified that five RMGs (NSUN7, NOP2, PUS1, PUS3 and FTO) could function as biomarkers for clinic diagnose of sepsis. For validation, we determined that the relative expressions of NSUN7, NOP2, PUS1 and PUS3 were upregulated, while FTO was downregulated in septic patients. The area under the ROC curve (AUC) of NSUN7, NOP2, PUS1, PUS3 and FTO was 0.828, 0.707, 0.846, 0.834 and 0.976, respectively. Conclusions: Our study uncovered that dysregulation of RNA methylation genes (m1A, m5C, m6Am, m7G and Ψ) was closely involved in the pathogenesis of sepsis, providing new insights into the classification of sepsis endotypes. We also revealed that five hub RMGs could function as novel diagnostic biomarkers and potential targets for treatment.

Cytotoxic CD161-CD8+ TEMRA cells contribute to the pathogenesis of systemic lupus erythematosus.

BACKGROUND: Systemic lupus erythematosus (SLE) is a prototypical autoimmune disease affecting multiple organs and tissues with high cellular heterogeneity. CD8+ T cell activity is involved in the SLE pathogenesis. However, the cellular heterogeneity and the underlying mechanisms of CD8+ T cells in SLE remain to be identified. METHODS: Single-cell RNA sequencing (scRNA-seq) of PBMCs from a SLE family pedigree (including 3 HCs and 2 SLE patients) was performed to identify the SLE-associated CD8+ T cell subsets. Flow cytometry analysis of a SLE cohort (including 23 HCs and 33 SLE patients), qPCR analysis of another SLE cohort (including 30 HCs and 25 SLE patients) and public scRNA-seq datasets of autoimmune diseases were employed to validate the finding. Whole-exome sequencing (WES) of this SLE family pedigree was used to investigate the genetic basis in dysregulation of CD8+ T cell subsets identified in this study. Co-culture experiments were performed to analyze the activity of CD8+ T cells. FINDINGS: We elucidated the cellular heterogeneity of SLE and identified a new highly cytotoxic CD8+ T cell subset, CD161-CD8+ TEMRA cell subpopulation, which was remarkably increased in SLE patients. Meanwhile, we discovered a close correlation between mutation of DTHD1 and the abnormal accumulation of CD161-CD8+ TEMRA cells in SLE. DTHD1 interacted with MYD88 to suppress its activity in T cells and DTHD1 mutation promoted MYD88-dependent pathway and subsequently increased the proliferation and cytotoxicity of CD161-CD8+ TEMRA cells. Furthermore, the differentially expressed genes in CD161-CD8+ TEMRA cells displayed a strong out-of-sample prediction for case-control status of SLE. INTERPRETATION: This study identified DTHD1-associated expansion of CD161-CD8+ TEMRA cell subpopulation is critical for SLE. Our study highlights genetic association and cellular heterogeneity of SLE pathogenesis and provides a mechanistical insight into the diagnosis and treatment of SLE. FUNDINGS: Stated in the Acknowledgements section of the manuscript.

Integrative analysis of omics summary data reveals putative mechanisms linked to different cell populations in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a complex disease involving many interactions at the molecular level, the details of which remain unclear. Here, we demonstrated an analytical paradigm of prioritizing genes and regulatory elements based on GWAS loci at the single-cell levels. Our initial step was to apply TWMR to identify causal genes and causal methylation sites in SLE. Based on the eQTL, LD and mQTL, we calculated the correlation between these genes and methylation sites. Next, we separately used gene expression and DNAm as exposure variables and outcome variables to analyze the regulatory mechanisms. We identified two mediating modes for SLE: 1) transcription mediation model and 2) epigenetic mediation model. Further, using single-cell RNA sequencing data, we revealed the cell subclusters associated with these mechanisms. Our identification of the mechanisms of SLE in different cell populations is of great significance for understanding the heterogeneity of disease in different cell populations.

Calculation of Similarity Between 26 Autoimmune Diseases Based on Three Measurements Including Network, Function, and Semantics.

Autoimmune diseases (ADs) are a broad range of diseases in which the immune response to self-antigens causes damage or disorder of tissues, and the genetic susceptibility is regarded as the key etiology of ADs. Accumulating evidence has suggested that there are certain commonalities among different ADs. However, the theoretical research about similarity between ADs is still limited. In this work, we first computed the genetic similarity between 26 ADs based on three measurements: network similarity (NetSim), functional similarity (FunSim), and semantic similarity (SemSim), and systematically identified three significant pairs of similar ADs: rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), myasthenia gravis (MG) and autoimmune thyroiditis (AIT), and autoimmune polyendocrinopathies (AP) and uveomeningoencephalitic syndrome (Vogt-Koyanagi-Harada syndrome, VKH). Then we investigated the gene ontology terms and pathways enriched by the three significant AD pairs through functional analysis. By the cluster analysis on the similarity matrix of 26 ADs, we embedded the three significant AD pairs in three different disease clusters respectively, and the ADs of each disease cluster might have high genetic similarity. We also detected the risk genes in common among the ADs which belonged to the same disease cluster. Overall, our findings will provide significant insight in the commonalities of different ADs in genetics, and contribute to the discovery of novel biomarkers and the development of new therapeutic methods for ADs.

Blood Genomics Identifies Three Subtypes of Systemic Lupus Erythematosus: "IFN-High," "NE-High," and "Mixed".

PURPOSE: Systemic lupus erythematosus (SLE) is a systemic and multifactorial autoimmune disease, and its diverse clinical manifestations affect molecular diagnosis and drug benefits. Our study was aimed at defining the SLE subtypes based on blood transcriptome data, analyzing functional patterns, and elucidating drug benefits. METHODS: Three data sets were used in this paper that were collected from the Gene Expression Omnibus (GEO) database, which contained two published data sets of pediatric and adult SLE patients (GSE65391, GSE49454) and public longitudinal data (GSE72754) from a cohort of SLE patients treated with IFN-α Kinoid (IFN-K). Based on disease activity scores and gene expression data, we defined a global SLE signature and merged three clustering algorithms to develop a single-sample subtype classifier (SSC). Systematic analysis of coexpression networks based on modules revealed the molecular mechanism for each subtype. RESULTS: We identified 92 genes as a signature of the SLE subtypes and three intrinsic subsets ("IFN-high," "NE-high," and "mixed"), which varied in disease severity. We speculated that IFN-high might be due to the overproduction of interferons (IFNs) caused by viral infection, leading to the formation of autoantibodies. NE-high might primarily result from bacterial and fungal infections that stimulated neutrophils (NE) to produce neutrophil extracellular traps (NETs) and induced individual autoimmune responses. The mixed type contained both of these molecular mechanisms and showed an intrinsic connection. CONCLUSIONS: Our research results indicated that identifying the molecular mechanism associated with different SLE subtypes would benefit the molecular diagnosis and stratified therapy. Moreover, repositioning of IFN-K based on subtypes also revealed an improved therapeutic effect, providing a new direction for disease treatment and drug development.

Identification of hub lncRNA ceRNAs in multiple sclerosis based on ceRNA mechanisms.

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system, and the pathogenesis is influenced by genetic susceptibility. Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) play essential roles in complex diseases, including acting as competing endogenous RNAs (ceRNAs). However, the functional roles and regulatory mechanisms of lncRNAs acting as ceRNAs in MS are still unclear. In this study, we identified hub lncRNA ceRNAs in MS based on ceRNA mechanisms and annotated their functions. The lncRNA-associated ceRNA network (LACN) was constructed by integrating the expression profiles of lncRNA/mRNA and miRNA in MS and normal samples, and the experimentally validated interactions of lncRNA-miRNA and mRNA-miRNA. We found three hub lncRNA ceRNAs (XIST, OIP5-AS1, and CTB-89H12.4) using the network analysis and obtained 96 lncRNA-mediated competing triplets (LCTs, lncRNA-miRNA-mRNA) with the hub lncRNA ceRNAs, which constituted 3 hub ceRNA modules. The functional analysis identified 12 pathways enriched by the 3 hub lncRNA ceRNAs, of which 6 were confirmed to be related to MS. For example, XIST was enriched in the 'spliceosome' and 'RNA transport' related to the typing of MS, and CTB-89H12.4 was enriched in the 'mTOR signaling pathway,' a potential therapeutic target for MS. We dissected the expression patterns of the 96 LCTs in MS individually. LCT XIST-miR-326-HNRNPA1, for which the expression pattern in MS revealed that XIST and HNRNPA1 were up-regulated and miR-326 was down-regulated, consisted of risk RNAs for MS that were validated by other research. Therefore, XIST-miR-326-HNRNPA1 might play a central role in the pathogenesis of MS. These results will contribute to the discovery of novel biomarkers and the development of new therapeutic methods for MS.