SAMD9 is a (epi-) genetically regulated anti-inflammatory factor activated in RA patients

Identification of novel biomarkers for childhood-onset systemic lupus erythematosus using machine learning algorithms and immune infiltration analysis

BACKGROUND

Childhood-onset systemic lupus erythematosus (cSLE) is a chronic autoimmune disease that is often more severe than adult-onset SLE and is challenging to diagnose due to its variable presentation and lack of specific diagnostic tests.

OBJECTIVES

This study aimed to identify potential diagnostic biomarkers for cSLE by analyzing differentially expressed genes (DEGs) using machine learning algorithms.

METHODS

In this study, we utilized the Gene Expression Omnibus database to investigate the DEGs between cSLE and normal samples, conducting a functional enrichment analysis on DEGs. Subsequently, we employed machine learning algorithms, including Least Absolute Shrinkage and Selection Operator regression and Support Vector Machine-Recursive Feature Elimination, to identify hub DEGs, which serve as crucial biomarkers. We delved into the role of these hub DEGs in the pathogenesis of the disease and the correlation between these hub DEGs and immune infiltration by comprehensive immune infiltration analysis using the CIBERSORT algorithm.

RESULTS

We identified 110 DEGs in cSLE, including 95 upregulated and 15 downregulated genes. Functional annotation revealed that these DEGs were involved in immune response processes, viral defense mechanisms, and regulation of interferon responses. Machine learning algorithms identified CCR1 and SAMD9L as hub DEGs, which were validated in multiple datasets and demonstrated high diagnostic value for cSLE. Mechanistic exploration suggested that CCR1 and SAMD9L are involved in immune response modulation, particularly in interferon signaling and the innate immune system. Assessment of immune cell infiltration revealed significant differences in immune cell composition between cSLE patients and healthy controls, with cSLE patients exhibiting a higher proportion of neutrophils. Moreover, CCR1 and SAMD9L expression levels showed positive correlations with neutrophil infiltration and other immune cell types.

CONCLUSION

CCR1 and SAMD9L were identified as potential diagnostic biomarkers for cSLE using machine learning and were validated in multiple datasets. These findings provide novel insights into the biological underpinnings of cSLE.

Identification of key genes as potential diagnostic and therapeutic targets for comorbidity of myasthenia gravis and COVID-19

Introduction

Myasthenia gravis (MG) is a chronic autoimmune neuromuscular disorder. Coronavirus disease 2019 (COVID-19) has a significant impact on the health and quality of life of MG patients and may even trigger the onset of MG in some cases. With the worldwide development of the COVID-19 vaccination, several new-onset MG cases and exacerbations following the COVID-19 vaccines have been acknowledged. The potential link between myasthenia gravis (MG) and COVID-19 has prompted the need for further investigation into the underlying molecular mechanism.

Methods and results

The differential expression analysis identified six differentially expressed genes (DEGs) shared by myasthenia gravis (MG) and COVID-19, namely SAMD9, PLEK, GZMB, JUNB, NR4A1, and NR1D1. The relationship between the six common genes and immune cells was investigated in the COVID-19 dataset. The predictive value of the shared genes was assessed and a nomogram was constructed using machine learning algorithms. The regulatory miRNAs, transcription factors and small molecular drugs were predicted, and the molecular docking was carried out by AutoDock.

Discussion

We have identified six common DEGs of MG and COVID-19 and explored their immunological effects and regulatory mechanisms. The result may provide new insights for further mechanism research.

SAMD1 attenuates antiphospholipid syndrome-induced pregnancy complications

OBJECTIVE

This study was intended to investigate the effect of SAMD1 on antiphospholipid syndrome (APS)-induced pregnancy complications in mice.

METHODS

The mRNA and protein expression of SAMD1 in APS patients and healthy controls was detected by qRT-PCR and western blot. Anti-B2 GPI and ACA levels were tested by ELISA, MMP-9, iNOS, ICAM-1 and MCP-1 mRNA and protein levels determined by qRT-PCR and western blot, cellular senescence detected by β-galactosidase staining, cell proliferation ability detected by CCK-8 assay, cell viability detected by trypan blue staining, cell mobility detected by Transwell, and cell angiogenesis ability detected by matrigel tube formation assay. An APS pregnant mouse model was constructed, and the embryo absorption rate was calculated.

RESULTS

SAMD1 expression was low in serum of APS patients, which was correlated with the history of thrombosis and the number of adverse pregnancies. Anti-B2 GPI and ACA levels were increased in APS. The expressions of MMP-9, iNOS, ICAM-1, and MCP-1 were also significantly upregulated in HUVECs treated with APS serum. APS promoted HUVEC senescence and inhibited cell proliferation, migration and angiogenesis. Overexpression of SAMD1 reversed the above results. Experiments on the APS pregnant mouse model confirmed that overexpression of SAMD1 reduced the rate of fetal loss.

CONCLUSION

SAMD1 may reduce APS-induced embryo loss by regulating cellular senescence, proliferation, migration, and angiogenesis.

Development and verification of a combined diagnostic model for primary Sjögren's syndrome by integrated bioinformatics analysis and machine learning

Primary Sjögren's syndrome (pSS) is a chronic, systemic autoimmune disease mostly affecting the exocrine glands. This debilitating condition is complex and specific treatments remain unavailable. There is a need for the development of novel diagnostic models for early screening. Four gene profiling datasets were downloaded from the Gene Expression Omnibus database. The 'limma' software package was used to identify differentially expressed genes (DEGs). A random forest-supervised classification algorithm was used to screen disease-specific genes, and three machine learning algorithms, including artificial neural networks (ANN), random forest (RF), and support vector machines (SVM), were used to build a pSS diagnostic model. The performance of the model was measured using its area under the receiver operating characteristic curve. Immune cell infiltration was investigated using the CIBERSORT algorithm. A total of 96 DEGs were identified. By utilizing a RF classifier, a set of 14 signature genes that are pivotal in transcription regulation and disease progression in pSS were identified. Through the utilization of training and testing datasets, diagnostic models for pSS were successfully designed using ANN, RF, and SVM, resulting in AUCs of 0.972, 1.00, and 0.9742, respectively. The validation set yielded AUCs of 0.766, 0.8321, and 0.8223. It was the RF model that produced the best prediction performance out of the three models tested. As a result, an early predictive model for pSS was successfully developed with high diagnostic performance, providing a valuable resource for the screening and early diagnosis of pSS.

A genome-wide association study of survival in patients with sepsis

BACKGROUND

Sepsis is a severe systemic inflammatory response to infections that is accompanied by organ dysfunction and has a high mortality rate in adult intensive care units. Most genetic studies have identified gene variants associated with development and outcomes of sepsis focusing on biological candidates. We conducted the first genome-wide association study (GWAS) of 28-day survival in adult patients with sepsis.

METHODS

This study was conducted in two stages. The first stage was performed on 687 European sepsis patients from the GEN-SEP network and 7.5 million imputed variants. Association testing was conducted with Cox regression models, adjusting by sex, age, and the main principal components of genetic variation. A second stage focusing on the prioritized genetic variants was performed on 2,063 ICU sepsis patients (1362 European Americans and 701 African-Americans) from the MESSI study. A meta-analysis of results from the two stages was conducted and significance was established at p < 5.0 × 10-8. Whole-blood transcriptomic, functional annotations, and sensitivity analyses were evaluated on the identified genes and variants.

FINDINGS

We identified three independent low-frequency variants associated with reduced 28-day sepsis survival, including a missense variant in SAMD9 (hazard ratio [95% confidence interval] = 1.64 [1.37-6.78], p = 4.92 × 10-8). SAMD9 encodes a possible mediator of the inflammatory response to tissue injury.

INTERPRETATION

We performed the first GWAS of 28-day sepsis survival and identified novel variants associated with reduced survival. Larger sample size studies are needed to better assess the genetic effects in sepsis survival and to validate the findings.

SAMD1 attenuates antiphospholipid syndrome-induced vascular injury and pregnancy complications

OBJECTIVE

This study was intended to investigate the effect of SAMD1 on antiphospholipid syndrome (APS)-induced vascular injury in human umbilical vein endothelial cells (HUVECs) and pregnancy complications in mice.

METHODS

The expression of SAMD1 in APS patients and healthy controls was detected by quantitative real-time polymerase chain reaction (qRT-PCR). Anti-B₂ GPI and anticardiolipin antibody (ACA) levels were tested by enzyme-linked immunosorbent assay, MMP-9, iNOS, ICAM-1, and MCP-1 mRNA and protein levels determined by qRT-PCR and Western blot, cellular senescence detected by β-galactosidase staining, cell proliferation ability detected by CCK-8 assay, cell viability detected by trypan blue staining, cell mobility detected by Transwell, and cell angiogenesis ability detected by matrigel tube formation assay. An APS pregnant mouse model was constructed, and the embryo absorption rate was calculated.

RESULTS

SAMD1 expression was low in serum of APS patients, which was correlated with the history of thrombosis and the number of adverse pregnancies. Anti-B₂ GPI and ACA levels were increased in APS. The expressions of MMP-9, iNOS, ICAM-1, and MCP-1 were also significantly upregulated in HUVECs treated with APS serum. APS promoted HUVEC senescence and inhibited cell proliferation, migration, and angiogenesis. Overexpression of SAMD1 reversed the above results. Experiments on the APS pregnant mouse model confirmed that overexpression of SAMD1 reduced the rate of fetal loss.

CONCLUSION

SAMD1 may reduce APS-induced vascular injury and embryo loss by regulating cellular senescence, proliferation, migration, and angiogenesis.

ITGA2 protein is associated with rheumatoid arthritis in Chinese and affects cellular function of T cells

BACKGROUND

We identified proteins significant for rheumatoid arthritis (RA) in peripheral blood mononuclear cells (PBMCs), and to clarify mechanisms mediated by underlying proteins that may involve in the pathogenesis of RA.

METHODS

Proteome-wide protein expressions were profiled by employing label-free quantitative proteomics methodology (Easy-nLC1000 and Q-exactive). The t-test was applied to identify differentially expressed proteins (DEP, p ≤ 0.05) between RA case and control samples. Gene Ontology enrichment analyses and Protein-Protein Interaction analyses were performed to annotate functions of DEPs. The selected DEP was validated in independent samples using Simple Western assay. Plasma protein level of α2 component of integrin (ITGA2) was measured by using ELISA. The DEP, ITGA2, was assessed for its effects on T cell proliferation, cell cycle, apoptosis, and inflammatory cytokine expression.

RESULTS

Sixty-four DEPs (p < 0.05) were identified in PBMCs. The selected ITGA2 (Fold Change, FC = 2.20, p = 1.49E-02) was validated to be up-regulated (FC = 12.33, p = 4.90E-2) with RA, and plasma ITGA2 protein level significantly elevated in RA patients vs. controls. Over-expression of ITGA2 could promote proliferation and inhibit apoptosis of Jurkat T cell, and induce IL-8, IFN-γ and TNF-α expression in Jurkat T cells.

CONCLUSIONS

ITGA2 protein was significantly over-expressed in PBMCs in RA patients, and affects T cell growth and pro-inflammatory cytokine expression in T cells.

Investigating genetic drivers of juvenile dermatomyositis pathogenesis using bioinformatics methods

Juvenile dermatomyositis (JDM) is a chronic autoimmune disease. The pathogenic mechanisms remain ill-defined. The purpose of this study was to identify key genes related to JDM. Microarray datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEG) were identified. Then, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and protein-protein interaction (PPI) network were carried out. In addition, the hub genes were selected by cytoHubba. The expression profile and diagnostic capacity (receiver-operator curve [ROC]) of interested hub genes were verified. Gene set enrichment analysis (GSEA) was also carried out. Moreover, the signature of hub genes was then used as a search query to explore the Connectivity Map (CMAP). A total of 128 DEG were identified. The enriched functions and pathways of the DEG include response to virus, negative regulation of cell migration, cadmium ion transmembrane transport, defense response to Gram-negative bacterium, positive regulation of megakaryocyte differentiation, and negative regulation of angiogenesis. Twenty-one hub genes were identified. The expression levels of the interested genes were also confirmed. ROC analysis confirmed that the expression of these genes can distinguish JDM from controls. GSEA showed that these genes are mainly related to "inflammatory response", "complement", "interferon-α response", "IL6/JAK/STAT3 signaling", "TGF-β signaling", "IL2/STAT5 signaling" and "TNF-α signaling via NF-κB". The CMAP research found some compounds with the potential to counteract the effects of the dysregulated molecular signature in JDM. In this study, bioinformatics methods were used to identify DEG, which helps us understand the molecular mechanisms of JDM and provide candidate targets for diagnosis and treatment of JDM.

Association of novel rare coding variants with juvenile idiopathic arthritis

OBJECTIVE

Juvenile idiopathic arthritis (JIA) is the most common type of arthritis among children, but a few studies have investigated the contribution of rare variants to JIA. In this study, we aimed to identify rare coding variants associated with JIA for the genome-wide landscape.

METHODS

We established a rare variant calling and filtering pipeline and performed rare coding variant and gene-based association analyses on three RNA-seq datasets composed of 228 JIA patients in the Gene Expression Omnibus against different sets of controls, and further conducted replication in our whole-exome sequencing (WES) data of 56 JIA patients. Then we conducted differential gene expression analysis and assessed the impact of recurrent functional coding variants on gene expression and signalling pathway.

RESULTS

By the RNA-seq data, we identified variants in two genes reported in literature as JIA causal variants, as well as additional 63 recurrent rare coding variants seen only in JIA patients. Among the 44 recurrent rare variants found in polyarticular patients, 10 were replicated by our WES of patients with the same JIA subtype. Several genes with recurrent functional rare coding variants have also common variants associated with autoimmune diseases. We observed immune pathways enriched for the genes with rare coding variants and differentially expressed genes.

CONCLUSION

This study elucidated a novel landscape of recurrent rare coding variants in JIA patients and uncovered significant associations with JIA at the gene pathway level. The convergence of common variants and rare variants for autoimmune diseases is also highlighted in this study.

Single-cell analyses identify dysfunctional CD16+ CD8 T cells in smokers

Tobacco smoke exposure contributes to the global burden of communicable and chronic diseases. To identify the immune cells affected by smoking, we use single-cell RNA sequencing on peripheral blood from smokers and nonsmokers. Transcriptomes reveal a subpopulation of FCGR3A (CD16)-expressing natural killer (NK)-like CD8 T lymphocytes that increase in smokers. Mass cytometry confirms elevated CD16⁺ CD8 T cells in smokers. Inferred as highly differentiated by pseudotime analysis, NK-like CD8 T cells express markers that are characteristic of effector memory re-expressing CD45RA T (T_EMRA) cells. Indicative of immune aging, smokers’ CD8 T cells are biased toward differentiated cells, and smokers have fewer naive cells than nonsmokers. DNA methylation-based models show that smoking dose is associated with accelerated aging and decreased telomere length, a biomarker of T cell senescence. Immune aging accompanies T cell senescence, which can ultimately lead to impaired immune function. This suggests a role for smoking-induced, senescence-associated immune dysregulation in smoking-mediated pathologies.

Smoking shifts the composition of CD8 T cells from naive to differentiated states

NK-like CD16⁺ CD8 T_EMRA cells are elevated in smokers and express GZMB and PRF1

DNA methylation links smoking dose with age acceleration and shortened telomeres

CD8 T, CD4 T, NKT, NK, and monocytes express senescence-linked genes in smokers

Epigenetically regulated co-expression network of genes significant for rheumatoid arthritis

Aim: To identify epigenetically regulated network of genes in peripheral blood mononuclear cells significant for rheumatoid arthritis (RA). Methods: Differentially expressed genes (DEGs) and their associated differentially expressed miRNAs and differentially methylated positions (DMPs) were identified. Causal inference test (CIT) identified the causal regulation chains. The analyses, for example, weighted gene co-expression network (WGCNA), protein-protein interaction and functional enrichment, evaluated interaction patterns among the DEGs and the associated epigenetic factors. Results: A total of 181 DEGs were identified. The DEGs were significantly regulated by DMPs and/or differentially expressed miRNAs. Causal inference test analyses identified 18 causal chains of DMP-DEG-RA and 16 intermediate DEGs enriched in 'protein kinase inhibitor activity'. BTN2A1 was co-expressed with other 9 intermediate genes and 11 known RA-associated genes and played a pivotal role in the co-expression network. Conclusion: Epigenetically regulated network of genes in peripheral blood mononuclear cells (PBMC) contributed to RA. The causal DMPs and key intermediate genes may serve as potential biomarkers for RA.

Autoimmune diseases in China

Autoimmune diseases, such as rheumatoid arthritis, systematic lupus erythematosus and Sjögren's syndrome, are a group of diseases characterized by the activation of immune cells and excessive production of autoantibodies. Although the pathogenesis of these diseases is still not completely understood, studies have shown that multiple factors including genetics, environment and immune responses play important roles in the development and progression of the diseases. In China, there are great achievements in the mechanisms of autoimmune diseases during the last decades. These studies provide new insight to understand the diseases and also shed light on the development of novel therapy.