Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis

PANoptosis signaling enables broad immune response in psoriasis: From pathogenesis to new therapeutic strategies

Background

Accumulating evidence suggests that regulated cell death, such as pyroptosis, apoptosis, and necroptosis, is deeply involved in the pathogenesis of psoriasis. As a newly recognized form of systematic cell death, PANoptosis is involved in a variety of inflammatory disorders through amplifying inflammatory and immune cascades, but its role in psoriasis remains elusive.

Objectives

To reveal the role of PANoptosis in psoriasis for a potential therapeutic strategy.

Methods

Multitranscriptomic analysis and experimental validation were used to identify PANoptosis signaling in psoriasis. RNA-seq and scRNA-seq analyses were performed to establish a PANoptosis-mediated immune response in psoriasis, which revealed hub genes through WGCNA and predicted disulfiram as a potential drug. The effect and mechanism of disulfiram were verified in imiquimod (IMQ)-induced psoriasis.

Results

Here, we found a highlighted PANoptosis signature in psoriasis patients through multitranscriptomic analysis and experimental validation. Based on this, two distinct PANoptosis patterns (non/high) were identified, which were the options for clinical classification. The high-PANoptosis-related group had a higher response rate to immune cell infiltration (such as M1 macrophages and keratinocytes). Subsequently, WGCNA showed the hub genes (e.g., S100A12, CYCS, NOD2, STAT1, HSPA4, AIM2, MAPK7), which were significantly associated with clinical phenotype, PANoptosis signature, and identified immune response in psoriasis. Finally, we explored disulfiram (DSF) as a candidate drug for psoriasis through network pharmacology, which ameliorated IMQ-mediated psoriatic symptoms through antipyroptosis-mediated inflammation and enhanced apoptotic progression. By analyzing the specific ligand-receptor interaction pairs within and between cell lineages, we speculated that DSF might exert its effects by targeting keratinocytes directly or targeting M1 macrophages to downregulate the proliferation of keratinocytes.

Conclusions

PANoptosis with its mediated immune cell infiltration provides a roadmap for research on the pathogenesis and therapeutic strategies of psoriasis.

Elevated expression of hsa_circ_0000479 in neutrophils correlates with features of systemic lupus erythematosus

OBJECTIVE

Accumulating evidence suggests that differentially expressed circular RNAs (circRNAs) play critical roles in immune cells of systemic lupus erythematosus (SLE) patients. Hsa_circ_0000479 has been studied in the field of cancer and infection, whereas seldom studied in autoimmune diseases. The aim of this study was to investigate the role and clinical value of neutrophil hsa_circ_0000479 in SLE.

METHODS

The expression levels of hsa_circ_0000479 in both healthy individuals and SLE patients' neutrophils were detected by qPCR and compared with those in peripheral blood mononuclear cells (PBMCs) . In addition, the correlation of hsa_circ_0000479 levels in neutrophils with the clinical and immunological features of SLE patients was also analysed.

RESULTS

The expression levels of hsa_circ_0000479 in the patients with SLE were significantly higher in neutrophils than that of PBMCs, and also significantly higher than that in healthy controls (HCs). Moreover, the expression levels of hsa_circ_0000479 in neutrophils were negatively associated with absolute neutrophil count and complement 3 (C3), whereas positively correlated with anti-dsDNA and anti-nucleosome antibodies in SLE. In addition, SLE patients with higher levels of hsa_circ_0000479 demonstrated more several clinical manifestations, including Raynaud's phenomenon, alopecia and leucopenia.

CONCLUSIONS

Hsa_circ_0000479 is up-regulated in neutrophils of SLE patients, and is also associated with several important laboratory indicators and clinical manifestations, suggesting that hsa_circ_0000479 in neutrophils was one of probable factors involved in the pathogenesis of SLE with potential clinical value.

Cerebroprotein hydrolysate-I ameliorates cognitive dysfunction in APP/PS1 mice by inhibiting ferroptosis via the p53/SAT1/ALOX15 signalling pathway

Ferroptosis, an iron-dependent lipid peroxidation-driven cell death pathway, has been linked to the development of Alzheimer's disease (AD). However, the role of ferroptosis in the pathogenesis of AD remains unclear. Cerebroprotein hydrolysate-I (CH-I) is a mixture of peptides with neurotrophic effects that improves cognitive deficits and reduces amyloid burden. The present study investigated the ferroptosis-induced signalling pathways and the neuroprotective effects of CH-I in the brains of AD transgenic mice. Seven-month-old male APPswe/PS1dE9 (APP/PS1) transgenic mice were treated with intraperitoneal injections of CH-I and saline for 28 days. The Morris water maze test was used to assess cognitive function. CH-I significantly improved cognitive deficits and attenuated beta-amyloid (Aβ) aggregation and tau phosphorylation in the hippocampus of APP/PS1 mice. RNA sequencing revealed that multiple genes and pathways, including ferroptosis-related pathways, were involved in the neuroprotective effects of CH-I. The increased levels of lipid peroxidation, ferrous ions, reactive oxygen species (ROS), and altered expression of ferroptosis-related genes (recombinant solute carrier family 7, member 11 (SLC7A11), spermidine/spermine N1-acetyltransferase 1 (SAT1) and glutathione peroxidase 4 (GPX4)) were significantly alleviated after CH-I treatment. Quantitative real-time PCR and western blotting were performed to investigate the expression of key ferroptosis-related genes and the p53/SAT1/arachidonic acid 15-lipoxygenase (ALOX15) signalling pathway. The p53/SAT1/ALOX15 signalling pathway was found to be involved in mediating ferroptosis, and the activation of this pathway was significantly suppressed in AD by CH-I. CH-I demonstrated neuroprotective effects against AD by attenuating ferroptosis and the p53/SAT1/ALOX15 signalling pathway, thus providing new targets for AD treatment.

Identification of coagulation diagnostic biomarkers related to the severity of spinal cord injury

BACKGROUND

Blood always shows coagulation changes after spinal cord injury (SCI), and identifying these blood changes may be helpful for diagnosis and treatment of SCI. Nevertheless, studies to date on blood coagulation changes after SCI in humans are not comprehensive. Therefore, this study aims to identify blood coagulation diagnostic biomarkers and immune changes related to SCI and its severity levels.

METHODS

Human blood sequencing datasets were obtained from public databases. Differentially expressed coagulation-related genes were analyzed (DECRGs). Enrichment analysis and assessment of immune changes were conducted. Weighted gene co-expression network analysis, least absolute shrinkage and selection operator logistic regression were used to identify biomarkers. Validation for these biomarkers was performed. The correlation between biomarkers and immune cells was evaluated. Transcription factors, miRNA, lncRNA, and drugs that can regulate biomarkers were analyzed.

RESULTS

DECRGs associated with SCI and its different grades were identified, showing enrichment in altered coagulation and immune-related signaling pathways. ADAM9, CD55, and STAT4 were identified as coagulation diagnostic biomarkers for SCI. IRF4 and PABPC4 were identified as coagulation diagnostic biomarkers for American Spinal Injury Association Impairment Scale (AIS) A grade of SCI. GP9 was designated as a diagnostic biomarker for AIS D grade of SCI. Immune changes in blood of SCI and its different grades were observed. Correlation between diagnostic biomarkers and immune cells were identified. Transcription factors, miRNA, lncRNA, and drugs that can regulate diagnostic biomarker expression were discovered.

CONCLUSION

Therefore, detecting the expression of these putative diagnostic biomarkers and related immune changes may be helpful for predicting the severity of SCI. Uncovering potential regulatory mechanisms for biomarkers may be beneficial for further research.

DNA methylation and whole-genome transcription analysis in CD4+ T cells from systemic lupus erythematosus patients with or without renal damage

BACKGROUND

Lupus nephritis (LN) is the most common cause of kidney injury in systemic lupus erythematosus (SLE) patients and is associated with increased mortality. DNA methylation, one of the most important epigenetic modifications, has been reported as a key player in the pathogenesis of SLE. Hence, our article aimed to explore DNA methylation in CD4+ T cells from LNs to identify additional potential biomarkers and pathogenic genes involved in the progression of LN.

METHODS

Our study enrolled 46 SLE patients with or without kidney injury and 23 healthy controls from 2019 to 2022. CD4+ T cells were sorted for DNA methylation genotyping and RNA-seq. Through bioinformatics analysis, we identified the significant differentially methylated CpG positions (DMPs) only in the LN group and validated them by Bisulfite PCR. Integration analysis was used to screen for differentially methylated and expressed genes that might be involved in the progression of LN, and the results were analyzed via cell experiments and flow cytometry.

RESULTS

We identified 243 hypomethylated sites and 778 hypermethylated sites only in the LN cohort. Three of these DMPs, cg08332381, cg03297029, and cg16797344, were validated by Bisulfite PCR and could be potential biomarkers for LN. Integrated analysis revealed that the expression of BCL2L14 and IFI27 was regulated by DNA methylation, which was validated by azacytidine (5-aza) treatment. The overexpression of BCL2L14 in CD4+ T cells might induce renal fibrosis and inflammation by regulating the differentiation and function of Tfh cells.

CONCLUSION

Our study identified novel aberrant DMPs in CD4+ T cells only in LN patients and DNA methylation-regulated genes that could be potential LN biomarkers. BCL2L14 is likely involved in the progression of LN and might be a treatment target.

Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2

Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease

The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.

LncRNA SNHG1 serves as a biomarker for systemic lupus erythematosus and participates in the disease progression

LncRNAs play an important role in autoimmune diseases. The purpose of this study was to explore the role of lncRNA SNHG1 in systemic lupus erythematosus (SLE), and laid a theoretical foundation for the study of SLE. The basic clinical information of all subjects was first collected for statistical analysis, and SNHG1 expression in the serum of all subjects was detected by RT-qPCR. The value of SNHG1 in the diagnosis of SLE was assessed by ROC. The correlation between SNHG1 and each blood sample index was analyzed by Pearson correlation analysis. The role of SNHG1 in primary peripheral blood mononuclear cells (PBMCs) apoptosis was explored. SNHG1 expression is relatively upregulated in patients with SLE compared to healthy people. SNHG1 expression was positively correlated with SLEDAI score, IgG, CRP, and ESR, and negatively correlated with C3 and C4. ROC indicated that SNHG1 has the potential to assist in the diagnosis of SLE. PBMCs apoptosis in SLE was higher than that in control group, the knockdown and overexpression of SNHG1 could correspondingly inhibit and promote PBMCs apoptosis. SNHG1 has the potential to be a diagnosis marker for SLE and may be involved in regulating PBMCs apoptosis.

DNA methylation of IFI44L as a potential blood biomarker for childhood-onset systemic lupus erythematosus

BACKGROUND

IFN-induced protein 44-like (IFI44L) promoter methylation has been demonstrated to serve as an effective blood diagnostic biomarker for adult-onset SLE. However, its utility as a diagnostic marker for childhood-onset SLE (cSLE) remains to be verified.

METHODS

Initially, we conducted a differential analysis of gene methylation and mRNA expression patterns in cSLE whole blood samples obtained from the public GEO database to determine IFI44L gene expression and assess the methylation status at its CpG sites. Subsequently, we collected clinical whole blood samples from 49 cSLE patients and 12 healthy children, employing an HRM-qPCR-based IFI44L methylation detection technique to evaluate its diagnostic efficacy in pediatric clinical practice.

RESULTS

A total of 26 hypomethylated, highly expressed genes in cSLE were identified by intersecting differentially expressed genes (DEGs) and differentially methylation genes (DMGs). GO enrichment analysis for these 26 genes indicated a robust association with type I IFN. Among the overlapping genes, IFI44L exhibited the most pronounced differential expression and methylation. In subsequent clinical validation experiments, IFI44L methylation was confirmed as an effective blood-based diagnostic biomarker for cSLE, achieving an AUC of 0.867, a sensitivity of 0.753, and a specificity of 1.000.

CONCLUSIONS

IFI44L methylation is a promising blood biomarker for cSLE.

IMPACT

IFI44L promoter methylation was reported to serve as a highly sensitive and specific diagnostic marker for adult-onset SLE. However, the diagnostic efficacy of IFI44L in childhood-onset SLE (cSLE) still remains to be confirmed. In this study, we utilized bioinformatics analysis and conducted clinical experiments to demonstrate that IFI44L methylation can also serve as a promising blood biomarker for cSLE. The findings of this study can facilitate the diagnosis of cSLE and broaden our understanding of its molecular mechanisms, with a particular focus on those related to type I interferons.

IFN-signaling gene expression as a diagnostic biomarker for monogenic interferonopathies

IFN-signaling gene (ISG) expression scores are potential markers of inflammation with significance from cancer to genetic syndromes. In Aicardi Goutières Syndrome (AGS), a disorder of abnormal DNA and RNA metabolism, this score has potential as a diagnostic biomarker, although the approach to ISG calculation has not been standardized or validated. To optimize ISG calculation and validate ISG as a diagnostic biomarker, mRNA levels of 36 type I IFN response genes were quantified from 997 samples (including 334 AGS), and samples were randomized into training and test data sets. An independent validation cohort (n = 122) was also collected. ISGs were calculated using all potential combinations up to 6 genes. A 4-gene approach (IFI44L, IFI27, USP18, IFI6) was the best-performing model (AUC of 0.8872 [training data set], 0.9245 [test data set]). The majority of top-performing gene combinations included IFI44L. Performance of IFI44L alone was 0.8762 (training data set) and 0.9580 (test data set) by AUC. The top approaches were able to discriminate individuals with genetic interferonopathy from control samples. This study validates the context of use for the ISG score as a diagnostic biomarker and underscores the importance of IFI44L in diagnosis of genetic interferonopathies.

Multi-omics analysis uncovered systemic lupus erythematosus and COVID-19 crosstalk

BACKGROUND

Studies have highlighted a possible crosstalk between the pathogeneses of COVID-19 and systemic lupus erythematosus (SLE); however, the interactive mechanisms remain unclear. We aimed to elucidate the impact of COVID-19 on SLE using clinical information and the underlying mechanisms of both diseases.

METHODS

RNA-seq datasets were used to identify shared hub gene signatures between COVID-19 and SLE, while genome-wide association study datasets were used to delineate the interaction mechanisms of the key signaling pathways. Finally, single-cell RNA-seq datasets were used to determine the primary target cells expressing the shared hub genes and key signaling pathways.

RESULTS

COVID-19 may affect patients with SLE through hematologic involvement and exacerbated inflammatory responses. We identified 14 shared hub genes between COVID-19 and SLE that were significantly associated with interferon (IFN)-I/II. We also screened and obtained four core transcription factors related to these hub genes, confirming the regulatory role of the IFN-I/II-mediated Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway on these hub genes. Further, SLE and COVID-19 can interact via IFN-I/II and IFN-I/II receptors, promoting the levels of monokines, including interleukin (IL)-6/10, tumor necrosis factor-α, and IFN-γ, and elevating the incidence rate and risk of cytokine release syndrome. Therefore, in SLE and COVID-19, both hub genes and core TFs are enriched within monocytes/macrophages.

CONCLUSIONS

The interaction between SLE and COVID-19 promotes the activation of the IFN-I/II-triggered JAK-STAT signaling pathway in monocytes/macrophages. These findings provide a new direction and rationale for diagnosing and treating patients with SLE-COVID-19 comorbidity.

Unveiling shared biomarkers and therapeutic targets between systemic lupus erythematosus and heart failure through bioinformatics analysis

Background

Systemic lupus erythematosus (SLE) is frequently accompanied by various complications, with cardiovascular diseases being particularly concerning due to their high mortality rate. Although there is clinical evidence suggesting a potential correlation between SLE and heart failure (HF), the underlying shared mechanism is not fully understood. Therefore, it is imperative to explore the potential mechanisms and shared therapeutic targets between SLE and HF.

Methods

The SLE and HF datasets were downloaded from the NCBI Gene Expression Omnibus database. Differentially expressed genes (DEGs) in both SLE and HF were performed using "limma" R package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genes (KEGG) analyses were conducted to analyze the enriched functions and pathways of DEGs in both SLE and HF datasets. Protein-Protein Interaction network (PPI) and the molecular complex detection (MCODE) plugins in the Cytoscape software were performed to identify the shared hub genes between SLE and HF datasets. R package "limma" was utilized to validate the expression of hub genes based on SLE (GSE122459) and HF (GSE196656) datasets. CIBERSORT algorithm was utilized to analyze the immune cell infiltration of SLE and HF samples based on SLE (GSE112087) and HF (GSE116250) datasets. A weighted gene co-expression network analysis (WGCNA) network was established to further validate the hub genes based on HF dataset (GSE116250). Molecular biology techniques were conducted to validate the hub genes.

Results

999 shared DGEs were identified between SLE and HF datasets, which were mainly enriched in pathways related to Th17 cell differentiation. 5 shared hub genes among the common DGEs between SLE and HF datasets were screened and validated, including HSP90AB1, NEDD8, RPLP0, UBB, and UBC. Additionally, 5 hub genes were identified in the central part of the MEbrown module, showing the strongest correlation with dilated cardiomyopathy. HSP90AB1 and UBC were upregulated in failing hearts compared to non-failing hearts, while UBB, NEDD8, and RPLP0 did not show significant changes.

Conclusion

HSP90AB1 and UBC are closely related to the co-pathogenesis of SLE and HF mediated by immune cell infiltration. They serve as promising molecular markers and potential therapeutic targets for the treatment of SLE combined with HF.

Potential mechanism of Chai Gui Zexie Decoction for NSCLC treatment assessed using network pharmacology, bioinformatics, and molecular docking: An observational study

To explore the potential mechanism of Chai Gui Zexie Decoction for non-small cell lung cancer (NSCLC) treatment using network pharmacology, bioinformatics, and molecular docking. The active ingredients of Chai Gui Zexie Decoction and the associated predicted targets were screened using the TCMSP database. NSCLC-related targets were obtained from GeneCards and OMIM. Potential action targets, which are intersecting drug-predicted targets and disease targets, were obtained from Venny 2.1. The protein-protein interaction network was constructed by importing potential action targets into the STRING database, and the core action targets and core ingredients were obtained via topological analysis. The core action targets were entered into the Metascape database, and Gene Ontology annotation analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed. Differentially expressed genes were screened using the Gene Expression Omnibus, and the key targets were obtained by validating the core action targets. The key targets were input into The Tumor IMmune Estimation Resource for immune cell infiltration analysis. Finally, the molecular docking of key targets and core ingredients was performed. We obtained 60 active ingredients, 251 drug prediction targets, and 2133 NSCLC-related targets. Meanwhile, 147 potential action targets were obtained, and 47 core action targets and 40 core ingredients were obtained via topological analysis. We detected 175 pathways related to NSCLC pharmaceutical therapy. In total, 1249 Gene Ontology items were evaluated. Additionally, 3102 differential genes were screened, and tumor protein P53, Jun proto-oncogene, interleukin-6, and mitogen-activated protein kinase 3 were identified as the key targets. The expression of these key targets in NSCLC was correlated with macrophage, CD4+ T, CD8+ T, dendritic cell, and neutrophil infiltration. The molecular docking results revealed that the core ingredients have a potent affinity for the key targets. Chai Gui Zexie Decoction might exert its therapeutic effect on NSCLC through multiple ingredients, targets, and signaling pathways.

Analysis and validation of biomarkers of immune cell-related genes in postmenopausal osteoporosis: An observational study

Postmenopausal osteoporosis (PMOP) is a common metabolic inflammatory disease. In conditions of estrogen deficiency, chronic activation of the immune system leads to a hypo-inflammatory phenotype and alterations in its cytokine and immune cell profile, although immune cells play an important role in the pathology of osteoporosis, studies on this have been rare. Therefore, it is important to investigate the role of immune cell-related genes in PMOP. PMOP-related datasets were downloaded from the Gene Expression Omnibus database. Immune cells scores between high bone mineral density (BMD) and low BMD samples were assessed based on the single sample gene set enrichment analysis method. Subsequently, weighted gene co-expression network analysis was performed to identify modules highly associated with immune cells and obtain module genes. Differential analysis between high BMD and low BMD was also performed to obtain differentially expressed genes. Module genes are intersected with differentially expressed genes to obtain candidate genes, and functional enrichment analysis was performed. Machine learning methods were used to filter out the signature genes. The receiver operating characteristic (ROC) curves of the signature genes and the nomogram were plotted to determine whether the signature genes can be used as a molecular marker. Gene set enrichment analysis was also performed to explore the potential mechanism of the signature genes. Finally, RNA expression of signature genes was validated in blood samples from PMOP patients and normal control by real-time quantitative polymerase chain reaction. Our study of PMOP patients identified differences in immune cells (activated dendritic cell, CD56 bright natural killer cell, Central memory CD4 T cell, Effector memory CD4 T cell, Mast cell, Natural killer T cell, T follicular helper cell, Type 1 T-helper cell, and Type 17 T-helper cell) between high and low BMD patients. We obtained a total of 73 candidate genes based on modular genes and differential genes, and obtained 5 signature genes by least absolute shrinkage and selection operator and random forest model screening. ROC, principal component analysis, and t-distributed stochastic neighbor embedding down scaling analysis revealed that the 5 signature genes had good discriminatory ability between high and low BMD samples. A logistic regression model was constructed based on 5 signature genes, and both ROC and column line plots indicated that the model accuracy and applicability were good. Five signature genes were found to be associated with proteasome, mitochondria, and lysosome by gene set enrichment analysis. The real-time quantitative polymerase chain reaction results showed that the expression of the signature genes was significantly different between the 2 groups. HIST1H2AG, PYGM, NCKAP1, POMP, and LYPLA1 might play key roles in PMOP and be served as the biomarkers of PMOP.

SLC27A2 is a potential immune biomarker for hematological tumors and significantly regulates the cell cycle progression of diffuse large B-cell lymphoma

BACKGROUND

Research on the fatty acid metabolism related gene SLC27A2 is currently mainly focused on solid tumors, and its mechanism of action in hematological tumors has not been reported.

METHOD

This study aims to explore the pathological and immune mechanisms of the fatty acid metabolism related gene SLC27A2 in hematological tumors and verify its functional role in hematological tumors through cell experiments to improve treatment decisions and clinical outcomes of hematological tumors.

RESULT

This study identified the fatty acid metabolism related gene SLC27A2 as a common differentially expressed gene between DLBCL and AML. Immune microenvironment analysis showed that SLC27A2 was significantly positively correlated with T cell CD4 + , T cell CD8 + , endothelial cells, macrophages, and NK cells in DLBCL. In AML, there is a significant negative correlation between SLC27A2 and B cells, T cell CD8 + , and macrophages. SLC27A2 participates in the immune process of hematological tumors through T cell CD8 + and macrophages. The GESA results indicate that high expression of SLC27A2 is mainly involved in the fatty acid pathway, immune pathway, and cell cycle pathway of DLBCL. The low expression of SLC27A2 is mainly involved in the immune pathway of AML. Therefore, SLC27A2 is mainly involved in the pathological mechanisms of hematological tumors through immune pathways, and cell experiments have also confirmed that SLC27A2 is involved in the regulation of DLBCL cells.

CONCLUSION

In summary, our research results comprehensively report for the first time the mechanism of action of SLC27A2 in the immune microenvironment of DLBCL and AML, and for the first time verify the cycle and apoptotic effects of the fatty acid related gene SLC27A2 in DLBCL cells through cell experiments. Research can help improve the treatment of AML and DLBCL patients.

Genetic insights into superior grain number traits: a QTL analysis of wheat-Agropyron cristatum derivative pubing3228

BACKGROUND

Agropyron cristatum (L.) is a valuable genetic resource for expanding the genetic diversity of common wheat. Pubing3228, a novel wheat-A. cristatum hybrid germplasm, exhibits several desirable agricultural traits, including high grain number per spike (GNS). Understanding the genetic architecture of GNS in Pubing3228 is crucial for enhancing wheat yield. This study aims to analyze the specific genetic regions and alleles associated with high GNS in Pubing3228.

METHODS

The study employed a recombination inbred line (RIL) population derived from a cross between Pubing3228 and Jing4839 to investigate the genetic regions and alleles linked to high GNS. Quantitative Trait Loci (QTL) analysis and candidate gene investigation were utilized to explore these traits.

RESULTS

A total of 40 QTLs associated with GNS were identified across 16 chromosomes, accounting for 4.25-17.17% of the total phenotypic variation. Five QTLs (QGns.wa-1D, QGns.wa-5 A, QGns.wa-7Da.1, QGns.wa-7Da.2 and QGns.wa-7Da.3) accounter for over 10% of the phenotypic variation in at least two environments. Furthermore, 94.67% of the GNS QTL with positive effects originated from Pubing3228. Candidate gene analysis of stable QTLs identified 11 candidate genes for GNS, including a senescence-associated protein gene (TraesCS7D01G148000) linked to the most significant SNP (AX-108,748,734) on chromosome 7D, potentially involved in reallocating nutrients from senescing tissues to developing seeds.

CONCLUSION

This study provides new insights into the genetic mechanisms underlying high GNS in Pubing3228, offering valuable resources for marker-assisted selection in wheat breeding to enhance yield.

MicroRNA-98-5p Inhibits IFI44L-Mediated Differentiation of Dendritic Cells and Activation of Interferon Pathway in Systemic Lupus Erythematosus

MicroRNA-98-5p (miR-98-5p) plays a protective role in the pathogenesis of autoimmune diseases through anti-inflammatory effects, but little is known about its role in Systemic lupus erythematosus (SLE). Our previous study suggested Interferon-inducible 44 like (IFI44L) overexpressed in monocytes which contributes to the pathogenesis of SLE by enhancing the maturation and functions of monocyte-derived dendritic cells (Mo-DCs), and miR-98-5p can regulate the expression of IFI44L. In this study, we identified miR-98-5p lowly expressed in both peripheral blood mononuclear cells (PBMCs) and monocytes of SLE patients along with high expression of IFI44L. IFI44L serves as target gene of miR-98-5p which inhibits differentiation of Mo-DCs and IFI44L-mediated activation of interferon pathway. We further showed that miR-98-5p promotes methylation of the IFI44L promoter to down-regulate its expression in SLE. Our results reveal an important role for miR-98-5p in the IFI44L-mediated immune imbalance of SLE and suggest a potential therapeutic target for SLE in the future.

Aberrant H3K4me3 modification of immune response genes in CD4+ T cells of patients with systemic lupus erythematosus

BACKGROUND

Increasing evidence has highlighted the significant role of histone modifications in pathogenesis of systemic lupus erythematosus (SLE). However, few studies have comprehensively analyzed trimethylation of histone H3 lysine 4 (H3K4me3) features at specific immune gene loci in SLE patients.

METHODS

We conducted H3K4me3 chromatin immunoprecipitation sequencing (ChIP-seq) on CD4+ T cells from SLE patients and healthy controls (HC). Differential H3K4me3 peaks were identified, followed by enrichment analysis. We integrated online RNA-seq and DNA methylation datasets to explore the relationship between H3K4me3 modification, DNA methylation and gene expression. We validated several upregulated peak regions by ChIP-qPCR and confirmed their impact on gene expression using RT-qPCR. Finally, we investigated the impact of H3K4 methyltransferases KMT2A on the expression of immune response genes.

RESULTS

we identified 147 downregulated and 2701 upregulated H3K4me3 peaks in CD4+ T cells of SLE. The upregulated peaks primarily classified as gained peaks and enriched in immune response genes such as FCGR2A, C5AR1, SERPING1 and OASL. Genes with upregulated H3K4me3 and downregulated DNA methylations in the promoter were highly expressed in SLE patients. These genes, including OAS1, IFI27 and IFI44L, were enriched in immune response pathways. The IFI44L locus also showed increased H3K27ac modification, chromatin accessibility and chromatin interactions in SLE. Moreover, knockdown of KMT2A can downregulate the expression of immune response genes in T cells.

CONCLUSION

Our study uncovers dysregulated H3K4me3 modification patterns in immune response genes loci, which also exhibit downregulated DNA methylation and higher mRNA expression in CD4+ T cells of SLE patients.

The role of cellular senescence-related genes in Asthma: Insights from bioinformatics and animal experiments

BACKGROUND

Asthma is a heterogeneous chronic respiratory disease, affecting about 10% of the global population. Cellular senescence is a multifaceted phenomenon defined as the irreversible halt of the cell cycle, commonly referred to as the senescence-associated secretory phenotype. Recent studies suggest that cellular senescence may play a role in asthma. This study aims to dissect the role and biological mechanisms of CSRGs in asthma, enhancing our understanding of the progression of asthma.

METHODS

The study utilized the GSE147878 dataset, employing methods like WGCNA, Differential analysis, Cibersort, GO, KEGG, unsupervised clustering, and GSVA to explore CSRGs functions and immune cell patterns in asthma. Machine learning identified key diagnostic genes, validated externally with the GSE165934 dataset and through qRT-PCR and WB experiments in animal models.

RESULT

From the GSE147878 dataset, 24 CSRGs were identified, highlighting their role in immune and inflammatory processes in asthma. Differences in CD4 naive T cells and activated dendritic cells between asthma and control groups underscored CSRGs' role in immune regulation. Cluster analysis revealed two distinct asthma patient groups with unique immune microenvironments. Machine learning identified five genes, leading to a TF-miRNA-mRNA network and singling out RHOA and RBM39 as key diagnostic genes, which were experimentally validated. Finally, a nomogram was created based on these genes.

CONCLUSION

This study, utilizing bioinformatics and animal experiments, identified RHOA and RBM39 as key diagnostic genes for asthma, providing new insights into the potential role and biological mechanisms of CSRGs in asthma.

Screening of Genes Associated with Immune Infiltration of Discoid Lupus Erythematosus Based on Weighted Gene Co-expression Network Analysis

Discoid lupus erythematosus (DLE) is a disorder of the immune system commonly seen in women of childbearing age. The pathophysiology and aetiology are still poorly understood, and no cure is presently available. Therefore, there is an urgent need to explore the underlying molecular mechanisms, as well as search for new therapeutic targets. Gene expression data from skin biopsies samples of DLE patients and healthy controls were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between DLE and healthy control samples were identified by differential expression analysis. Samples were analysed using CIBERSORT to examine the proportion of immune infiltration. Weighted gene co-expression network analysis was used to screen for the module most relevant to immune infiltration. Candidate genes were uploaded to the TRRUST database to obtain the potential transcription factors regulating these genes. Protein-protein interaction (PPI) analysis was performed to obtain the hub genes most associated with immune infiltration among the candidate genes. A total of 273 DEGs were identified between the DLE and healthy control samples. The results of immunoinfiltration analysis showed that the abundances of resting memory CD4 T cells, activated memory CD4 T cells and M1 macrophages were significantly higher, while those of resting infiltration of plasma cells, regulatory T cells and dendritic cells were lower in DLE samples than in healthy control samples. Correlation analysis showed that ISG15, TRIM22, XAF1, IFIT2, OAS2, OAS3, OAS1, IFI44, IFI6, BST2, IFIT1 and MX2 were negatively correlated with the abundances of plasma cells, T-cell regulatory cells and resting dendritic cells and positively correlated with activated memory CD4 T cells and M1 macrophages. Our study shows that these hub genes may regulate DLE via immune-related pathways mediated by the infiltration of these immune cells.

MULTIPLE MACHINE LEARNING METHODS AND COMPARATIVE TRANSCRIPTOMICS IDENTIFY PIVOTAL GENES FOR ISCHEMIA-REPERFUSION INJURY IN HUMAN DONOR TISSUE UNDERGOING ORTHOTOPIC LIVER TRANSPLANTATION

ABSTRACT

Background: Hepatic ischemia-reperfusion injury (HIRI) is a major complication affecting patient prognosis during the period after orthotopic liver transplantation (OLT). Although an increasing number of scientists have investigated the molecular biology of ischemia-reperfusion injury (IRI) during OLT in animal and cellular models in recent years, studies using comprehensive and high-quality sequencing results from human specimens to screen for key molecules are still lacking. Aims: The objective of this study is to explore the molecular biological pathways and key molecules associated with HIRI during OLT through RNA sequencing and related bioinformatics analysis techniques. Methods: The study was done by performing mRNA sequencing on liver tissue samples obtained from 15 cases of in situ liver transplantation patients who experienced ischemia and reperfusion injury within 1 year at Guizhou Medical University, and combined with bioinformatics analysis and machine learning methods, we identified the genes and transcription factors that are closely associated with IRI during in situ liver transplantation surgery. Results: There were 877 differentially expressed genes (DEGs) identified in the included liver samples, of which 817 DEGs were upregulated and 60 were downregulated. Functional enrichment analysis revealed significant enrichment of immune-related terms, such as inflammation, defense responses, responses to cytokines, immune system processes, and cellular activation. In addition, core gene enrichment analysis after cytoHubba screening suggested that liver reperfusion injury might be associated with translation-related elements as a pathway together with protein translation processes. Machine learning with the weighted correlation network analysis screening method identified PTGS2, IRF1, and CDKN1A as key genes in the reperfusion injury process. Conclusions: This study demonstrated that the pathways and genomes whose expression is altered throughout the reperfusion process might be critical for the progression of HIRI during OLT.

Identification of m6A-Related Biomarkers in Systemic Lupus Erythematosus: A Bioinformation-Based Analysis

Background

Systemic Lupus Erythematosus (SLE), a prototypical autoimmune disorder, presents a challenge due to the absence of reliable biomarkers for discerning organ-specific damage within SLE. A growing body of evidence underscores the pivotal involvement of N6-methyladenosine (m6A) in the etiology of autoimmune conditions.

Methods

The datasets, which primarily encompassed the expression profiles of m6A regulatory genes, were retrieved from the Gene Expression Omnibus (GEO) repository. The optimal model, selected from either Random Forest (RF) or Support Vector Machine (SVM), was employed for the development of a predictive nomogram model. To identify pivotal genes associated with SLE, a comprehensive screening process was conducted utilizing LASSO, SVM-RFE, and RF techniques. Within the realm of SLE susceptibility, Weighted Gene Co-expression Network Analysis (WGCNA) was harnessed to delineate relevant modules and hub genes. Additionally, MeRIP-qPCR assays were performed to elucidate key genes correlated with m6A targets. Furthermore, a Mendelian randomization study was conducted based on genome-wide association studies to assess the causative influence of MMP9 on ischemic stroke (IS), which is not only a severe cerebrovascular event but also a common complication of SLE.

Results

Twelve m6A regulatory genes was identified, demonstrating statistical significance (p < 0.05) and utilized for constructing a nomogram model using the RF algorithm. EPSTI1, USP18, HP, and MMP9, as the hub genes, were identified. MMP9 uniquely correlates with m6A modification and was causally linked to an increased risk of IS, as indicated by our inverse variance weighting analysis showing an odds ratio of 1.0134 (95% CI=1.0004-1.0266, p = 0.0440).

Conclusion

Our study identified twelve m6A regulators, shedding light on the molecular mechanisms underlying SLE risk genes. Importantly, our analysis established a causal relationship between MMP9, a key m6A-related gene, and ischemic stroke, a common complication of SLE, thereby providing critical insights for presymptomatic diagnostic approaches.

Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients' lives, but detecting severe SLE activity in its early stages is proving to be a formidable challenge. Consequently, this work advocates the use of Machine Learning (ML) algorithms for the diagnosis of SLE flares in the context of infections. In the pursuit of this research, the Random Forest (RF) method has been employed due to its performance attributes. With RF, our objective is to uncover patterns within the patient data. Multiple ML techniques have been scrutinized within this investigation. The proposed system exhibited around a 7.49% enhancement in accuracy when compared to k-Nearest Neighbors (KNN) algorithm. In contrast, the Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), Decision Trees (DT) and Linear Regression (LR) methods demonstrated inferior performance, with respective values around 81%, 78%, 84% and 69%. It is noteworthy that the proposed method displayed a superior area under the curve (AUC) and balanced accuracy (both around 94%) in comparison to other ML approaches. These outcomes underscore the feasibility of crafting an automated diagnostic support method for SLE patients grounded in ML systems.

The Construction and Validation of a Novel Ferroptosis-Related Gene Signature in Parkinson's Disease

As a newly discovered regulated cell death mode, ferroptosis is associated with the development of Parkinson's disease (PD) and has attracted much attention. Nonetheless, the relationship between ferroptosis and PD pathogenesis remains unclear. The GSE8397 dataset includes GPL96 and GPL97 platforms. The differential genes were analyzed by immune infiltration and Gene Set Enrichment Analysis (GSEA) (p < 0.05), and differential multiple |logFC| > 1 and weighted gene coexpression network analysis (WGCNA) were used to screen differential expression genes (DEGs). The intersection with 368 ferroptosis-related genes (FRGs) was conducted for gene ontology/Kyoto encyclopedia of gene and genome (GO/KEGG) enrichment analysis, gene expression analysis, correlation analysis, single-cell sequencing analysis, and prognosis analysis (area under the curve, AUC) and to predict relevant miRNAs and construct network diagrams using Cytoscape. The intersection genes of differentially expressed ferroptosis-related genes (DEFRGs) and mitochondrial dysfunction genes were validated in the substantia nigra of MPTP-induced PD mice models by Western blotting and immunohistochemistry, and the protein-binding pocket was predicted using the DoGSiteScorer database. According to the results, the estimated scores were positively correlated with the stromal scores or immune scores in the GPL96 and GPL97 platforms. In the GPL96 platform, the GSEA showed that differential genes were mainly involved in the GnRH signaling pathway, B cell receptor signaling pathway, inositol phosphate metabolism, etc. In the GPL97 platform, the GSEA showed that differential genes were mainly involved in the ubiquitin-mediated proteolysis, axon guidance, Wnt signaling pathway, MAPK signaling pathway, etc. We obtained 26 DEFRGs, including 12 up-regulated genes and 14 down-regulated genes, with good correlation. The area under the prognostic analysis curve (AUC > 0.700) showed a good prognostic ability. We found that they were enriched in different neuronal cells, oligodendrocytes, astrocytes, oligodendrocyte precursor cells, and microglial cells, and their expression scores were positively correlated, and selected genes with an AUC curve ≥0.9 were used to predict miRNA, including miR-214/761/3619-5p, miR-203, miR-204/204b/211, miR-128/128ab, miR-199ab-5p, etc. For the differentially expressed ferroptosis-mitochondrial dysfunction-related genes (DEF-MDRGs) (AR, ISCU, SNCA, and PDK4), in the substantia nigra of mice, compared with the Saline group, the expression of AR and ISCU was decreased (p < 0.05), and the expression of α-Syn and PDK4 was increased (p < 0.05) in the MPTP group. Therapeutic drugs that target SNCA include ABBV-0805, Prasinezumab, Cinpanemab, and Gardenin A. The results of this study suggest that cellular DEF-MDRGs might play an important role in PD. AR, ISCU, SNCA, and PDK4 have the potential to be specific biomarkers for the early diagnosis of PD.

Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning

BACKGROUND

Systemic lupus erythematosus (SLE) is a multifaceted autoimmune disease characterized by clinical and pathological diversity. Mitochondrial dysfunction has been identified as a critical pathogenetic factor in SLE. However, the specific molecular aspects and regulatory roles of this dysfunction in SLE are not fully understood. Our study aims to explore the molecular characteristics of mitochondria-related genes (MRGs) in SLE, with a focus on identifying reliable biomarkers for classification and therapeutic purposes.

METHODS

We sourced six SLE-related microarray datasets (GSE61635, GSE50772, GSE30153, GSE99967, GSE81622, and GSE49454) from the Gene Expression Omnibus (GEO) database. Three of these datasets (GSE61635, GSE50772, GSE30153) were integrated into a training set for differential analysis. The intersection of differentially expressed genes with MRGs yielded a set of differentially expressed MRGs (DE-MRGs). We employed machine learning algorithms-random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) logistic regression-to select key hub genes. These genes' classifying potential was validated in the training set and three other validation sets (GSE99967, GSE81622, and GSE49454). Further analyses included differential expression, co-expression, protein-protein interaction (PPI), gene set enrichment analysis (GSEA), and immune infiltration, centered on these hub genes. We also constructed TF-mRNA, miRNA-mRNA, and drug-target networks based on these hub genes using the ChEA3, miRcode, and PubChem databases.

RESULTS

Our investigation identified 761 differentially expressed genes (DEGs), mainly related to viral infection, inflammatory, and immune-related signaling pathways. The interaction between these DEGs and MRGs led to the identification of 27 distinct DE-MRGs. Key among these were FAM210B, MSRB2, LYRM7, IFI27, and SCO2, designated as hub genes through machine learning analysis. Their significant role in SLE classification was confirmed in both the training and validation sets. Additional analyses included differential expression, co-expression, PPI, GSEA, immune infiltration, and the construction of TF-mRNA, miRNA-mRNA, and drug-target networks.

CONCLUSIONS

This research represents a novel exploration into the MRGs of SLE, identifying FAM210B, MSRB2, LYRM7, IFI27, and SCO2 as significant candidates for classifying and therapeutic targeting.

Revealing common differential mRNAs, signaling pathways, and immune cells in blood, glomeruli, and tubulointerstitium of lupus nephritis patients based on transcriptomic data

Lupus nephritis (LN) is a potentially fatal autoimmune disease. The purpose of this study was to find potential key molecular markers of LN to aid in the early diagnosis and management of the disease. Datasets GSE99967_blood, GSE32591_glomeruli, and GSE32591_tubulointerstitium were included in this study. Differentially expressed mRNAs (DEmRNAs) were identified between the normal control and LN groups using the limma package in R. Common DEmRNAs in the three datasets were taken. Subsequently, functional enrichment analysis, immune correlation analysis, receiver operating characteristic (ROC) curve analysis and real-time polymerase chain reaction (RT-PCR) verification were performed. In this study, 11 common DEmRNAs were obtained and all of them were up-regulated. In protein-protein interaction (PPI) networks, we found that MX dynamin like GTPase 1 (MX1) and radical S-adenosyl methionine domain containing 2 (RSAD2) had the highest interaction score (0.997). Functional enrichment analysis revealed that MX1 and RSAD2 were enriched in influenza A and hepatitis C signaling pathways. The area under the curve (AUC) values of interferon-induced protein 44 (IFI44) and MX1 in GSE32591_glomeruli and GSE32591_tubulointerstitium datasets are 1, which is worthy of further study on their diagnostic value and molecular mechanism. The xCell analysis showed abnormal distribution of granulocyte-macrophage progenitor (GMP) cells in blood, glomeruli, and tubulointerstitium. Pearson's correlation analysis found that GMP cells were significantly correlated with lactotransferrin (LTF) and cell cycle. Identification of common DEmRNAs and key pathways in the blood, glomeruli, and tubulointerstitium of patients with LN provides potential research directions for exploring the molecular mechanisms of the disease.

Screening of potential biomarkers of system lupus erythematosus based on WGCNA and machine learning algorithms

Systemic lupus erythematosus (SLE) is an autoimmune disease involving multiple systems. Its recurrent episodes and fluctuating disease courses have a severe impact on patients. Biomarkers to predict disease prognosis and remission are still lacking in SLE. We downloaded the GSE50772 dataset from the Gene Expression Omnibus database and identified differentially expressed genes (DEGs) between SLE and healthy controls. Weighted gene co-expression network analysis was used to identify key gene modules and corresponding genes in SLE. The overlapped genes in DEGs and key modules are used as key genes for subsequent analysis. These key genes were analyzed using 3 machine learning algorithms, including the least absolute shrinkage and selection operator, support vector machine recursive elimination, and random forest algorithms. The overlapped genes were obtained as potential biomarkers for further analysis, investigating and validating the potential biomarkers' possible functions, regulatory mechanisms, diagnostic value, and expression levels. And finally studied the differences between groups in level of immune cell infiltration and explored the relationship between potential biomarkers and immunity. A total of 234 overlapped genes in DEGs and key modules are used as key genes for subsequent analysis. After taking the intersection of the key genes obtained by 3 algorithms, we got 4 potential biomarkers (ARID2, CYSTM1, DDIT3, and RNASE1) with high diagnostic values. Finally, further immune infiltration analysis showed differences in various immune cells in the SLE and healthy control samples. ARID2, CYSTM1, DDIT3, and RNASE1 can affect the immune function of SLE patients. ARID2, CYSTM1, DDIT3, and RNASE1 could be used as immune-related potential biomarkers and therapeutic or diagnostic targets for further research.

Identification of hub biomarkers and immune-related pathways participating in the progression of Kawasaki disease by integrated bioinformatics analysis

BACKGROUND

Kawasaki disease (KD) is a systemic vasculitis that commonly affects children and its etiology remains unknown. Growing evidence suggests that immune-mediated inflammation and immune cells in the peripheral blood play crucial roles in the pathophysiology of KD. The objective of this research was to find important biomarkers and immune-related mechanisms implicated in KD, along with their correlation with immune cells in the peripheral blood.

MATERIAL/METHODS

Gene microarray data from the Gene Expression Omnibus (GEO) was utilized in this study. Three datasets, namely GSE63881 (341 samples), GSE73463 (233 samples), and GSE73461 (279 samples), were obtained. To find intersecting genes, we employed differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). Subsequently, functional annotation, construction of protein-protein interaction (PPI) networks, and Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to identify hub genes. The accuracy of these hub genes in identifying KD was evaluated using the receiver operating characteristic curve (ROC). Furthermore, Gene Set Variation Analysis (GSVA) was employed to explore the composition of circulating immune cells within the assessed datasets and their relationship with the hub gene markers.

RESULTS

WGCNA yielded eight co-expression modules, with one hub module (MEblue module) exhibiting the strongest association with acute KD. 425 distinct genes were identified. Integrating WGCNA and DEGs yielded a total of 277 intersecting genes. By conducting LASSO analysis, five hub genes (S100A12, MMP9, TLR2, NLRC4 and ARG1) were identified as potential biomarkers for KD. The diagnostic value of these five hub genes was demonstrated through ROC curve analysis, indicating their high accuracy in diagnosing KD. Analysis of the circulating immune cell composition within the assessed datasets revealed a significant association between KD and various immune cell types, including activated dendritic cells, neutrophils, immature dendritic cells, macrophages, and activated CD8 T cells. Importantly, all five hub genes exhibited strong correlations with immune cells.

CONCLUSION

Activated dendritic cells, neutrophils, and macrophages were closely associated with the pathogenesis of KD. Furthermore, the hub genes (S100A12, MMP9, TLR2, NLRC4, and ARG1) are likely to participate in the pathogenic mechanisms of KD through immune-related signaling pathways.

B cell subsets-related biomarkers and molecular pathways for systemic lupus erythematosus by transcriptomics analyses

BACKGROUND

Systemic lupus erythematosus (SLE), an autoimmune disease, is characterised by B-cell abnormalities and a loss of tolerance that can produce autoantibody. However, the imperative genes and molecular pathways involved in the change of B cell populations remain unclear.

METHODS

The expression of B cell subsets between SLE and healthy controls (HCs) was detected based on micro-array transcriptome data. The Weighted Gene Co-Expression Network Analysis (WGCNA) further revealed the co-expression modules of naïve and memory B cells. Whereafter, we performed the functional enrichment analysis, Protein-protein interaction (PPI) networks construction and feature selection to screen hub genes. Ultimately, we recruited SLE patients and HCs from the Second Hospital of Shanxi Medical University and further verified these genes in transcriptome sequencing samples.

RESULTS

Total of 1087 SLE patients and 86 HCs constituted in the study. Compared to HCs, the levels of peripheral naïve B cells of SLE patients decreased, while memory B cells increased. WGCNA identified two modules with the highest correlation for the subsequent analysis. The purple module was primarily in connection with naïve B cells, and the GO analysis indicated that these genes were mainly abundant in B cell activation. The blue module relevant to memory B cells was most significantly enriched in the "defence response to virus" correlation pathway. Then we screened six hub genes by PPI and feature selection. Finally, four biomarkers (IFI27, IFITM1, MX2, IRF7) were identified by transcriptome sequencing verification.

CONCLUSION

Our study identified hub genes and key pathways associated with the naïve and memory B cells respectively, which may offer novel insights into the behaviours of B cells and the pathogenesis of SLE.

BM-MSCs display altered gene expression profiles in B-cell acute lymphoblastic leukemia niches and exert pro-proliferative effects via overexpression of IFI6

BACKGROUND

The tumor microenvironment (TME) is a supportive environment responsible for promoting the growth and proliferation of tumor cells. Current studies have revealed that the bone marrow mesenchymal stem cells (BM-MSCs), a type of crucial stromal cells in the TME, can promote the malignant progression of tumors. However, in the adult B-cell acute lymphoblastic leukemia (B-ALL) microenvironment, it is still uncertain what changes in BM-MSCs are induced by leukemia cells.

METHODS

In this study, we mimicked the leukemia microenvironment by constructing a BM-MSC-leukemia cell co-culture system. In vitro cell experiments, in vivo mouse model experiments, lentiviral transfection and transcriptome sequencing analysis were used to investigate the possible change of BM-MSCs in the leukemia niche and the potential factors in BM-MSCs that promote the progression of leukemia.

RESULTS

In the leukemia niche, the leukemia cells reduced the MSCs' capacity to differentiate towards adipogenic and osteogenic subtypes, which also promoted the senescence and cell cycle arrest of the MSCs. Meanwhile, compared to the mono-cultured MSCs, the gene expression profiles of MSCs in the leukemia niche changed significantly. These differential genes were enriched for cell cycle, cell differentiation, DNA replication, as well as some tumor-promoting biofunctions including protein phosphorylation, cell migration and angiogenesis. Further, interferon alpha-inducible protein 6 (IFI6), as a gene activated by interferon, was highly expressed in leukemia niche MSCs. The leukemia cell multiplication was facilitated evidently by IFI6 both in vitro and in vivo. Mechanistically, IFI6 might promote leukemia cell proliferation by stimulating SDF-1/CXCR4 axis, which leads to the initiation of downstream ERK signaling pathway. As suggested by further RNA sequencing analysis, the high IFI6 level in MSCs somewhat influenced the gene expression profile and biological functions of leukemia cells.

CONCLUSIONS

BM-MSCs in the leukemia niche have varying degrees of changes in biological characteristics and gene expression profiles. Overexpression of IFI6 in BM-MSCs could be a key factor in promoting the proliferation of B-ALL cells, and this effect might be exerted through the SDF-1/CXCR4/ERK signal stimulation. Targeting IFI6 or related signaling pathways might be an important measure to reduce the leukemia cell proliferation.

Shared biomarkers of multi-tissue origin for primary Sjogren's syndrome and their importance in immune microenvironment alterations

With the recent advancement in omics and molecular techniques, a wealth of new molecular biomarkers have become available for the diagnosis and classification of primary Sjögren's syndrome (pSS) patients. However, whether these biomarkers are universal is of great interest to us. In this study, we used various methods to obtain shared biomarkers derived from multiple tissue in pSS patients and to explore their relationship with immune microenvironment alterations. First we identified differentially expressed genes (DEGs) between pSS and healthy controls utilizing nine mRNA microarray datasets obtained from the Gene Expression Omnibus (GEO). Then, shared biomarkers were filtered out using robust rank aggregation (RRA), data integration analysis, weighted gene co-expression network analysis (WGCNA), and least absolute selection and shrinkage operator (LASSO) regression; their roles in pSS and association with changes in the immune microenvironment were also analyzed. In addition, these biomarkers were further confirmed with both the testing set and immunohistochemistry (IHC). As a result, ten biomarkers, i.e., EPSTI1, IFI44, IFIT1, IFIT2, IFIT3, MX1, OAS1, PARP9, SAMD9L and TRIM22, were identified. Receiver operating characteristic (ROC) curves showed that the ten genes could discriminate pSS from controls. Gene set enrichment analysis (GSEA) showed that the enrichment of immune-related gene sets was significant in pSS patients with high expression of either biomarker. Furthermore, the association between some immunocytes and these biomarkers was identified. In the two distinct molecular patterns of pSS patients based on the expressions of these biomarkers, the proportions of immunocytes were significantly different. Our study identified shared biomarkers of multi-tissue origin and revealed their relationship with altered immune microenvironment in pSS patients. These markers not only have diagnostic implications but also provide potential immunotherapeutic targets for the clinical treatment of pSS patients.

Classification of subtypes and identification of dysregulated genes in sepsis

Background

Sepsis is a clinical syndrome with high mortality. Subtype identification in sepsis is meaningful for improving the diagnosis and treatment of patients. The purpose of this research was to identify subtypes of sepsis using RNA-seq datasets and further explore key genes that were deregulated during the development of sepsis.

Methods

The datasets GSE95233 and GSE13904 were obtained from the Gene Expression Omnibus database. Differential analysis of the gene expression matrix was performed between sepsis patients and healthy controls. Intersection analysis of differentially expressed genes was applied to identify common differentially expressed genes for enrichment analysis and gene set variation analysis. Obvious differential pathways between sepsis patients and healthy controls were identified, as were developmental stages during sepsis. Then, key dysregulated genes were revealed by short time-series analysis and the least absolute shrinkage and selection operator model. In addition, the MCPcounter package was used to assess infiltrating immunocytes. Finally, the dysregulated genes identified were verified using 69 clinical samples.

Results

A total of 898 common differentially expressed genes were obtained, which were chiefly related to increased metabolic responses and decreased immune responses. The two differential pathways (angiogenesis and myc targets v2) were screened on the basis of gene set variation analysis scores. Four subgroups were identified according to median expression of angiogenesis and myc target v2 genes: normal, myc target v2, mixed-quiescent, and angiogenesis. The genes CHPT1, CPEB4, DNAJC3, MAFG, NARF, SNX3, S100A9, S100A12, and METTL9 were recognized as being progressively dysregulated in sepsis. Furthermore, most types of immune cells showed low infiltration in sepsis patients and had a significant correlation with the key genes. Importantly, all nine key genes were highly expressed in sepsis patients.

Conclusion

This study revealed novel insight into sepsis subtypes and identified nine dysregulated genes associated with immune status in the development of sepsis. This study provides potential molecular targets for the diagnosis and treatment of sepsis.

Pyruvate Dehydrogenase Kinase 1 inhibition mediated oxidative phosphorylation enhancement in cartilage promotes osteoarthritis progression

Osteoarthritis (OA) is a common disease characterized by cartilage degradation. Growing evidence showed that glucose metabolism impacts joint homeostasis and an imbalance between glycolysis and oxidative phosphorylation (OXPHOS) may exacerbate OA progression, however, a definitive link is yet to be established. Here, we report that pyruvate metabolism and oxidative phosphorylation pathway is enriched in OA cartilage through gene set enrichment analysis (GSEA) and expression of Pyruvate Dehydrogenase Kinase 1 (PDK1), an enzyme that can phosphorylate Pyruvate Dehydrogenase (PDH), and inhibit pyruvate fluxes into the tricarboxylic acid (TCA) cycle and to OXPHOS, in articular cartilage is notably reduced through destabilization of medial meniscus (DMM). Moreover, by inhibiting PDK1, cartilage loss is markedly accelerated in DMM-induced OA through extracellular matrix (ECM) degradation and apoptosis of chondrocytes. These results indicate that PDK1 is involved in the progression of OA through accelerating cartilage matrix degradation and synovium inflammation to ameliorate cartilage degeneration.

Identification of aging-related biomarkers and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning

Background

Osteoarthritis (OA) is a degenerative disease closely related to aging. Nevertheless, the role and mechanisms of aging in osteoarthritis remain unclear. This study aims to identify potential aging-related biomarkers in OA and to explore the role and mechanisms of aging-related genes and the immune microenvironment in OA synovial tissue.

Methods

Normal and OA synovial gene expression profile microarrays were obtained from the Gene Expression Omnibus (GEO) database and aging-related genes (ARGs) from the Human Aging Genomic Resources database (HAGR). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO), and Gene set variation analysis (GSVA) enrichment analysis were used to uncover the underlying mechanisms. To identify Hub ARDEGs with highly correlated OA features (Hub OA-ARDEGs), Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning methods were used. Furthermore, we created diagnostic nomograms and receiver operating characteristic curves (ROC) to assess Hub OA-ARDEGs' ability to diagnose OA and predict which miRNAs and TFs they might act on. The Single sample gene set enrichment analysis (ssGSEA) algorithm was applied to look at the immune infiltration characteristics of OA and their relationship with Hub OA-ARDEGs.

Results

We discovered 87 ARDEGs in normal and OA synovium samples. According to functional enrichment, ARDEGs are primarily associated with inflammatory regulation, cellular stress response, cell cycle regulation, and transcriptional regulation. Hub OA-ARDEGs with excellent OA diagnostic ability were identified as MCL1, SIK1, JUND, NFKBIA, and JUN. Wilcox test showed that Hub OA-ARDEGs were all significantly downregulated in OA and were validated in the validation set and by qRT-PCR. Using the ssGSEA algorithm, we discovered that 15 types of immune cell infiltration and six types of immune cell activation were significantly increased in OA synovial samples and well correlated with Hub OA-ARDEGs.

Conclusion

Synovial aging may promote the progression of OA by inducing immune inflammation. MCL1, SIK1, JUND, NFKBIA, and JUN can be used as novel diagnostic biomolecular markers and potential therapeutic targets for OA.

Higher CD4+CD40+ T cells (Th40 cells) associate with systemic lupus erythematosus activity

The aim of this study was to investigate the characteristics of CD4⁺CD40⁺ T cells (Th40 cells) in Chinese systemic lupus erythematosus (SLE) patients. Flow cytometry was used to identify the percentage of Th40 cells in peripheral blood from 24 SLE patients and 24 healthy individuals and the level of IL-2, IL-4, IL-6, IL-10, IFN-r, and TNF-α in serum (22 cases) from the SLE patients. Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2000) was used to assess the SLE disease active state. The percentage of Th40 cells in T cells from SLE patients (19.37 ± 17.43) (%) was significantly higher than that from healthy individuals (4.52 ± 3.16) (%) (P < 0.001). The percentage of Th40 cells was also positively associated with SLEDAI-2000 (P = 0.001) and negatively associated with complement C3 (P = 0.007). The Th40 cell percentage was different in SLE patients with different organs involved. The Th40 cell percentage in SLE patients with lupus serositis (29.29 ± 22.19) was significantly higher than that in patients without serositis (13.41 ± 10.79; P = 0.040), and the percentage in SLE patients with lupus pneumonia involvement (29.11 ± 11.88) was significantly higher than that in patients without lupus pneumonia (16.80 ± 17.99; P = 0.043). After 4 weeks treatment, the Th40 cell percentage decreased significantly (P = 0.005). However, Th40 cell expression was not related to cytokines (IL-2, IL-4, IL-6, IL-10, IFN-r, and TNF-α; P > 0.05). A significantly higher percentage of Th40 cells was found in SLE patients, and the Th40 cell percentage was associated with SLE activity. Thus, Th40 cells may be used as a predictor for SLE disease activity and severity and therapeutic efficacy.

Identification of Shared Immune Cells and Immune-Related Co-Disease Genes in Chronic Heart Failure and Systemic Lupus Erythematosus Based on Transcriptome Sequencing

Purpose

The purpose was to identify shared immune cells and co-disease genes in chronic heart failure (HF) and systemic lupus erythematosus (SLE), as well as explore the potential mechanisms of action between HF and SLE.

Methods

A collection of peripheral blood mononuclear cells (PBMCs) from ten patients with HF and SLE and ten normal controls (NC) was used for transcriptome sequencing. Differentially expressed genes (DEGs) analysis, enrichment analysis, immune infiltration analysis, weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and machine learning were applied for the screening of shared immune cells and co-disease genes in HF and SLE. Gene expression analysis and correlation analysis were used to explore the potential mechanisms of co-disease genes and immune cells in HF and SLE.

Results

In this study, it was found that two immune cells, T cells CD4 naïve and Monocytes, displayed similar expression patterns in HF and SLE at the same time. By taking intersection of the above immune cell-associated genes with the DEGs common to both HF and SLE, four immune-associated co-disease genes, CCR7, RNASE2, RNASE3 and CXCL10, were finally identified. CCR7, as one of the four key genes, was significantly down-regulated in HF and SLE, while the rest three key genes were all significantly up-regulated in both diseases.

Conclusion

T cells CD4 naïve and Monocytes were first revealed as possible shared immune cells of HF and SLE, and CCR7, RNASE2, RNASE3 and CXCL10 were identified as possible key genes common to HF and SLE as well as potential biomarkers or therapeutic targets for HF and SLE.

Deconvolution of whole blood transcriptomics identifies changes in immune cell composition in patients with systemic lupus erythematosus (SLE) treated with mycophenolate mofetil

BACKGROUND

Systemic lupus erythematosus (SLE) is a clinically and biologically heterogeneous autoimmune disease. We explored whether the deconvolution of whole blood transcriptomic data could identify differences in predicted immune cell frequency between active SLE patients, and whether these differences are associated with clinical features and/or medication use.

METHODS

Patients with active SLE (BILAG-2004 Index) enrolled in the BILAG-Biologics Registry (BILAG-BR), prior to change in therapy, were studied as part of the MASTERPLANS Stratified Medicine consortium. Whole blood RNA-sequencing (RNA-seq) was conducted at enrolment into the registry. Data were deconvoluted using CIBERSORTx. Predicted immune cell frequencies were compared between active and inactive disease in the nine BILAG-2004 domains and according to immunosuppressant use (current and past).

RESULTS

Predicted cell frequency varied between 109 patients. Patients currently, or previously, exposed to mycophenolate mofetil (MMF) had fewer inactivated macrophages (0.435% vs 1.391%, p = 0.001), naïve CD4 T cells (0.961% vs 2.251%, p = 0.002), and regulatory T cells (1.858% vs 3.574%, p = 0.007), as well as a higher proportion of memory activated CD4 T cells (1.826% vs 1.113%, p = 0.015), compared to patients never exposed to MMF. These differences remained statistically significant after adjusting for age, gender, ethnicity, disease duration, renal disease, and corticosteroid use. There were 2607 differentially expressed genes (DEGs) in patients exposed to MMF with over-representation of pathways relating to eosinophil function and erythrocyte development and function. Within CD4 + T cells, there were fewer predicted DEGs related to MMF exposure. No significant differences were observed for the other conventional immunosuppressants nor between patients according disease activity in any of the nine organ domains.

CONCLUSION

MMF has a significant and persisting effect on the whole blood transcriptomic signature in patients with SLE. This highlights the need to adequately adjust for background medication use in future studies using whole blood transcriptomics.

Identification and validation of SOCS1/2/3/4 as potential prognostic biomarkers and correlate with immune infiltration in glioblastoma

Suppressor of cytokine signalling (SOCS) 1/2/3/4 are involved in the occurrence and progression of multiple malignancies; however, their prognostic and developmental value in patients with glioblastoma (GBM) remains unclear. The present study used TCGA, ONCOMINE, SangerBox3.0, UALCAN, TIMER2.0, GENEMANIA, TISDB, The Human Protein Atlas (HPA) and other databases to analyse the expression profile, clinical value and prognosis of SOCS1/2/3/4 in GBM, and to explore the potential development mechanism of action of SOCS1/2/3/4 in GBM. The majority of analyses showed that SOCS1/2/3/4 transcription and translation levels in GBM tissues were significantly higher than those in normal tissues. qRT-PCR, western blotting (WB) and immunohistochemical staining were used to verify that SOCS3 was expressed at higher mRNA and protein levels in GBM than in normal tissues or cells. High SOCS1/2/3/4 mRNA expression was associated with poor prognosis in patients with GBM, especially SOCS3. SOCS1/2/3/4 were highly contraindicated, which had few mutations, and were not associated with clinical prognosis. Furthermore, SOCS1/2/3/4 were associated with the infiltration of specific immune cell types. In addition, SOCS3 may affect the prognosis of patients with GBM through JAK/STAT signalling pathway. Analysis of the GBM-specific protein interaction (PPI) network showed that SOCS1/2/3/4 were involved in multiple potential carcinogenic mechanisms of GBM. In addition, colony formation, Transwell, wound healing and western blotting assays revealed that inhibition of SOCS3 decreased the proliferation, migration and invasion of GBM cells. In conclusion, the present study elucidated the expression profile and prognostic value of SOCS1/2/3/4 in GBM, which may provide potential prognostic biomarkers and therapeutic targets for GBM, especially SOCS3.

AURKA, TOP2A and MELK are the key genes identified by WGCNA for the pathogenesis of lung adenocarcinoma

The comprehensive analysis of single or multiple microarray datasets is currently available in Gene Expression Omnibus (GEO) databases, with several studies having identified genes strongly associated with the development of lung adenocarcinoma (LUAD). However, the mechanisms of LUAD development remain largely unknown and has not yet been systematically studied; thus, further studies are required in this field. In the present study, weighted gene co-expression network analysis (WGCNA) was used for the evaluation of key genes with potential high risk of LUAD, and to provide more reliable evidence concerning its pathogenesis. The GSE140797 dataset from the high-throughput GEO database was downloaded and was first analyzed using the Limma package in the R language in order to determine the differentially expressed genes. The dataset was then analyzed using the WGCNA package to analyze the co-expressed genes, and the modular genes with the highest correlation with the clinical phenotype were identified. Subsequently, the pathogenic genes shared in common between the result of the two analyses were imported into the STRING database for protein-protein interaction network analysis. The hub genes were screened out using Cytoscape, and then The Cancer Genome Atlas analysis, receiver operating characteristic analysis and survival analysis were subsequently performed. Finally, the key genes were evaluated using reverse transcription-quantitative PCR and western blot analysis. Bioinformatics analysis of the GSE140797 dataset revealed eight key genes: AURKA, BUB1, CCNB1, CDK1, MELK, NUSAP1, TOP2A and PBK. Finally, the AURKA, TOP2A and MELK genes were evaluated in samples from patients with lung cancer using WGCNA and RT-qPCR, western blot analysis experiments, providing basis for further research on the mechanisms of LUAD development and targeted therapy.

Identification of necroptosis-related genes in Parkinson's disease by integrated bioinformatics analysis and experimental validation

Background

Parkinson's disease (PD) is the second most common neurodegeneration disease worldwide. Necroptosis, which is a new form of programmed cell death with high relationship with inflammation, plays a vital role in the progression of PD. However, the key necroptosis related genes in PD are not fully elucidated.

Purpose

Identification of key necroptosis-related genes in PD.

Method

The PD associated datasets and necroptosis related genes were downloaded from the GEO Database and GeneCards platform, respectively. The DEGs associated with necroptosis in PD were obtained by gap analysis, and followed by cluster analysis, enrichment analysis and WGCNA analysis. Moreover, the key necroptosis related genes were generated by PPI network analysis and their relationship by spearman correlation analysis. Immune infiltration analysis was used for explore the immune state of PD brain accompanied with the expression levels of these genes in various types of immune cells. Finally, the gene expression levels of these key necroptosis related genes were validated by an external dataset, blood samples from PD patients and toxin-induced PD cell model using real-time PCR analysis.

Result

Twelve key necroptosis-related genes including ASGR2, CCNA1, FGF10, FGF19, HJURP, NTF3, OIP5, RRM2, SLC22A1, SLC28A3, WNT1 and WNT10B were identified by integrated bioinformatics analysis of PD related dataset GSE7621. According to the correlation analysis of these genes, RRM2 and WNT1 were positively and negatively correlated with SLC22A1 respectively, while WNT10B was positively correlated with both OIF5 and FGF19. As the results from immune infiltration analysis, M2 macrophage was the highest population of immune cell in analyzed PD brain samples. Moreover, we found that 3 genes (CCNA1, OIP5 and WNT10B) and 9 genes (ASGR2, FGF10, FGF19, HJURP, NTF3, RRM2, SLC22A1, SLC28A3 and WNT1) were down- and up- regulated in an external dataset GSE20141, respectively. All the mRNA expression levels of these 12 genes were obviously upregulated in 6-OHDA-induced SH-SY5Y cell PD model while CCNA1 and OIP5 were up- and down- regulated, respectively, in peripheral blood lymphocytes of PD patients.

Conclusion

Necroptosis and its associated inflammation play fundamental roles in the progression of PD and these identified 12 key genes might be served as new diagnostic markers and therapeutic targets for PD.

Behçet syndrome: The disturbed balance between anti- (CLEC12A, CLC) and proinflammatory (IFI27) gene expressions

INTRODUCTION

Behçet syndrome (BS) is a chronic, multisystemic inflammatory condition with unanswered questions regarding its pathogenesis and rational therapeutics. A microarray-based comparative transcriptomic analysis was performed to elucidate the molecular mechanisms of BS and identify any potential therapeutic targets.

METHODS

Twenty-nine BS patients (B) and 15 age and sex-matched control subjects (C) were recruited. Patients were grouped as mucocutaneous (M), ocular (O), and vascular (V) according to their clinical phenotypes. GeneChip Human Genome U133 Plus 2.0 arrays were used for expression profiling on peripheral blood samples of the patients and the control subjects. Following documentation of the differentially expressed gene (DEG) sets, the data were further evaluated with bioinformatics analysis, visualization, and enrichment tools. Validation of the microarray data was performed using quantitative reverse transcriptase polymerase chain reaction.

RESULTS

When p ≤ 0.05 and fold change ≥2.0 were chosen, the following numbers of DEGs were obtained; B versus C: 28, M versus C: 20, O versus C: 8, V versus C: 555, M versus O: 6, M versus V: 324, O versus V: 142. Venn diagram analysis indicated only two genes, CLEC12A and IFI27, in the intersection of M versus C ∩ O versus C ∩ V versus C. Another noteworthy gene appeared as CLC in the DEG sets. Cluster analyses successfully clustered distinct clinical phenotypes of BS. While innate immunity-related processes were enriched in the M group, adaptive immunity-specific processes were significantly enriched in the O and V groups.

CONCLUSIONS

Distinct clinical phenotypes of BS patients displayed distinct expression profiles. In Turkish BS patients, expression differences regarding the genes CLEC12A, IFI27, and CLC seemed to be operative in the disease pathogenesis. Based on these findings, future research should consider the immunogenetic heterogeneity of BS clinical phenotypes. Two anti-inflammatory genes, namely CLEC12A and CLC, may be valuable as therapeutic targets and may also help design an experimental model in BS.

IFIH1 was predicted as a key biomarker in primary Sjögren's syndrome based on transcriptome analysis and experimental verification in patients and mice

OBJECTIVES

To investigate the novel key genes and biological processes that may lead to primary Sjögren' s syndrome (pSS).

METHODS

We downloaded datasets about peripheral blood samples of pSS patients and healthy controls (GSE51092, GSE84844, and GSE66795) from Gene Expression Omnibus database. The weighted co-expression network analysis and differential expression analysis first were implemented. After that, protein-protein network interaction and Support Vector Machines were applied in the meantime to take intersection for key genes. Moreover, we conducted immune cell infiltration analysis to explore the relationship between the gene expression and concentration of immune cells in peripheral blood. Lastly, the expression of key genes was verified in pSS patients and murine models by reverse-transcription polymerase chain reaction. Meanwhile, correlation analysis of gene expression and disease activity was also performed.

RESULTS

Only 1 key gene, interferon induced with helicase c domain 1 (IFIH1), was identified to be both significantly up-regulated and important for the diagnosis of pSS. The increased expression of IFIH1 in peripheral blood was confirmed in data sets, patients and non-obese diabetic (NOD) mice. Its expression was correlated with disease activity in patients as well. In addition, the IFIH1 expression was also increased in spleen and salivary glands infiltrated with lymphocytes in NOD mice. Furthermore, immune cell infiltration analysis showed that the expression of IFIH1 was positively correlated with the proportion of memory B cells and activated dendritic cells, and negatively correlated with the proportion of macrophage M0.

CONCLUSIONS

Here, bioinformatics analyses and experimental assays were performed to provide a new insight for understanding of pSS. IFIH1 may be a new diagnostic marker or therapeutic target for pSS.

Involvement of RNA methylation modification patterns mediated by m7G, m6A, m5C and m1A regulators in immune microenvironment regulation of Sjögren's syndrome

Keratoconjunctivitis is the most common complication of Sjögren's syndrome (SS). It has always been a hot research topic due to its complex pathogenesis. A further understanding of keratoconjunctiva xerosis can be obtained by studying the primary diseases. 7-Methylguanine (m7G), N6-methyladenosine (m6A), 5-methylcytosine (m5C), and N1-methyladenosine (m1A) are newly discovered epigenetic mechanisms involved in the development of SS. This study aimed to investigate the effects of m7G, m6A, m5C, and m1A modifications on the immune microenvironment of SS. Three microarray datasets were downloaded from the Gene Omnibus Expression (GEO) database, including 56 SS samples and 35 normal samples. Then, genes with m7G, m6A, m5C, and m1A methylation were explored, and the RNA modification patterns mediated by 59 m7G, m6A, m5C, and m1A regulators were summarized. The effects of m7G, m6A, m5C, and m1A modifications on immune infiltrating cells were discussed. Eukaryotic translation initiation factor 3 subunit D(EIF3D) was closely related to monocytes, and the expression of EIF3D was higher in SS with less monocytes. Two distinct patterns of RNA modification mediated by the 59 m7G, m6A, m5C, and m1A regulators were also identified, which infiltrated immune cells differently. Moreover, the two distinct RNA patterns were enriched in different signaling pathways, and their biological functions were explored. The findings revealed that m7G, m6A, m5C, and m1A modifications played vital roles in the diversity and complexity of the immune microenvironment in SS.

Novel polymorphism of IFI44L associated with the susceptibility and clinical characteristics of systemic lupus erythematosus in a Chinese population

BACKGROUND

Interferon-inducible 44 like (IFI44L) is a newly discovered gene which has been reported to associate with the susceptibility of some infectious diseases, but there is no data on IFI44L SNP polymorphism associated with Systemic lupus erythematosus (SLE). In this study, we aimed to evaluate the association of IFI44L rs273259 polymorphism with the susceptibility and clinical characteristics of SLE in a Chinese population.

METHODS

576 SLE patients and 600 controls were recruited in this case-control study. Blood DNA was extracted and IFI44L rs273259 polymorphism was detected by TaqMan SNP Genotyping Assay Kit. The expression levels of IFI44L in Peripheral blood mononuclear cells were detected by RT-qPCR. The DNA methylation levels of IFI44L promoter were detected by bisulfite pyrosequencing.

RESULTS

The genotype and allele frequencies of IFI44L rs273259 in SLE patients have a significantly difference compared to healthy controls (P < 0.001). The genotype AG (vs. AA: OR = 2.849; P < 0.001) and the allele G (vs. A: OR = 1.454; P < 0.001) were associated with increased susceptibility of SLE. IFI44L rs273259 polymorphism was associated with clinical characteristics of SLE including malar rash (P < 0.001), discoid rash (P < 0.001), lupus nephritis (P < 0.001) and anti-Smith antibodies (P < 0.001). The expression levels of IFI44L were most significantly increased in genotype AG than genotype AA and GG (P < 0.01). The DNA methylation levels of IFI44L promoter were most significantly decreased in genotype AG than genotype AA and GG (P < 0.01).

CONCLUSIONS

Our results indicate novel polymorphism of IFI44L rs273259 was associated with the susceptibility and clinical characteristics of SLE in the Chinese population.

Biomarker-driven development of new therapies for autoimmune diseases: current status and future promises

INTRODUCTION

Auto-immune diseases are complex and heterogeneous. Various types of biomarkers can be used to support precision medicine approaches to autoimmune diseases, ensuring that the right patient receives the most appropriate therapy to improve treatment outcomes.

AREAS COVERED

We review the recent progress made in modeling several autoimmune diseases such as Systemic Lupus Erythematosus, primary Sjogren Syndrome, and Rheumatoid Arthritis following extensive molecular profiling of large cohorts of patients. From this knowledge, BMKs are being identified which support diagnostic as well as patient stratification and prediction of response to treatment. The identification of biomarkers should be initiated early in drug development and properly validated during subsequent clinical trials. To ensure the robustness and reproducibility of biomarkers, the PERMIT Consortium recently established recommendations highlighting the importance of relevant study design, sample size, and appropriate validation of analytical methods.

EXPERT OPINION

The integration by AI-powered analytics of massive data provided by multi-omics technologies, high-resolution medical imaging and sensors borne by patients will eventually allow the identification of clinically relevant BMKs, likely in the form of combinatorial predictive algorithms, to support future drug development for autoimmune diseases.

Expression pattern and prognostic value of key regulators for N7-methylguanosine RNA modification in prostate cancer

Alterations in the regulators of RNA methylation modifications, such as N7-methylguanosine (m7G), have been implicated in a variety of diseases. Therefore, the analysis and identification of disease-related m7G modification regulators will accelerate advances in understanding disease pathogenesis. However, the implications of alterations in the regulators of m7G modifications remain poorly understood in prostate adenocarcinoma. In the present study, we analyze the expression patterns of 29 m7G RNA modification regulators in prostate adenocarcinoma using The Cancer Genome Atlas (TCGA) and perform consistent clustering analysis of differentially expressed genes (DEGs). We find that 18 m7G-related genes are differentially expressed in tumor and normal tissues. In different cluster subgroups, DEGs are mainly enriched in tumorigenesis and tumor development. Furthermore, immune analyses demonstrate that patients in cluster 1 have significantly higher scores for stromal and immune cells, such as B cells, T cells, and macrophages. Then, a TCGA-related risk model is developed and successfully validated using a Gene Expression Omnibus external dataset. Two genes ( EIF4A1 and NCBP2) are determined to be prognostically significant. Most importantly, we construct tissue microarrays from 26 tumor specimens and 20 normal specimens, and further confirm that EIF4A1 and NCBP2 are associated with tumor progression and Gleason score. Therefore, we conclude that the m7G RNA methylation regulators may be involved in the poor prognosis of patients with prostate adenocarcinoma. The results of this study may provide support for exploring the underlying molecular mechanisms of m7G regulators, especially EIF4A1 and NCBP2.

Identification of PDCD1 as a potential biomarker in acute rejection after kidney transplantation via comprehensive bioinformatic analysis

Background

Acute rejection is a determinant of prognosis following kidney transplantation. It is essential to search for novel noninvasive biomarkers for early diagnosis and prompt treatment.

Methods

Gene microarray data was downloaded from the Gene Expression Omnibus (GEO) expression profile database and the intersected differentially expressed genes (DEGs) was calculated. We conducted the DEGs with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Distribution of immune cell infiltration was calculated by CIBERSORT. A hub gene marker was identified by intersecting the rejection-related genes from WGCNA and a selected KEGG pathway-T cell receptor signaling pathway (hsa04660), and building a protein-protein interaction network using the STRING database and Cytoscape software. We performed flow-cytometry analysis to validate the hub gene.

Results

A total of 1450 integrated DEGs were obtained from five datasets (GSE1563, GSE174020, GSE98320, GSE36059, GSE25902). The GO, KEGG and immune infiltration analysis results showed that AR was mainly associated with T cell activation and various T-cell related pathways. Other immune cells, such as B cells, Macrophage and Dendritic cells were also associated with the progress. After utilizing the WGCNA and PPI network, PDCD1 was identified as the hub gene. The flow-cytometry analysis demonstrated that both in CD4⁺ and CD8⁺ T cells, PD1⁺CD57^-, an exhausted T cell phenotype, were downregulated in the acute rejection whole blood samples.

Conclusions

Our study illustrated that PDCD1 may be a candidate diagnostic biomarker for acute kidney transplant rejection via integrative bioinformatic analysis.

Identification of potential biomarkers for systemic lupus erythematosus by integrated analysis of gene expression and methylation data

INTRODUCTION

Systemic lupus erythematosus (SLE) is a heterogeneous and chronic autoimmune disease. Aberrant DNA methylation occurs during various processes of SLE development regulating the mRNA expression of interrelated genes. This study aims to screen potential DNA methylation markers for SLE.

METHODS

Gene expression and methylation datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between SLE patients and healthy controls were screened using the limma R package, and differentially methylated positions (DMPs) and regions (DMRs) were identified using dmpfinder and bumphunter (minfi). Additionally, the DNA methylation markers to distinguish SLE patients from healthy controls were explored through receiver operating characteristic (ROC) curves and logistic regression analyses. Finally, we validated the results of the bioinformatic analysis by pyrosequencing.

RESULTS

In total, 91 DEGs, 90,092 DMPs, 15 DMRs, and 13 DMR-associated genes were identified. Through the integrative analysis of DEG- and DMR-associated genes, we identified five type I interferon (IFN)-related genes as key epigenetic-driven genes in SLE. GO enrichment analysis showed that the five SLE-associated epigenetic-driven genes were mainly enriched in the type I IFN signaling pathway involved in immune response and defense response to virus. Moreover, we identified two SLE-specific DNA methylation markers, three SLE without lupus nephritis (SLE-LN^-)-specific DNA methylation markers, and two SLE with lupus nephritis (SLE-LN⁺)-specific DNA methylation markers by stepwise logistic regression.

CONCLUSIONS

Overall, our study demonstrates potential DNA methylation markers of SLE, SLE-LN^-, and SLE-LN⁺, which may help the diagnosis, boost the development of new epigenetic therapy, and contribute to individualized treatment. Key Points • This study identified five type I IFN-related genes as key epigenetic-driven genes in SLE, which support the importance of the type I IFN pathway in the pathogenesis of SLE • We identified novel DNA methylation biomarkers in SLE, SLE-LN-, and SLE-LN+ by a comprehensive analysis of bioinformatics methods and executed experimental validation, and binary logistic regression analysis showed that they have excellent potential • These results may provide new insights into the biological mechanisms of SLE, and identify reliable biomarkers for SLE, SLE-LN-, and SLE-LN+, which may contribute to diagnosis and individualized treatment.

A Predictive Disease Risk Model for Ankylosing Spondylitis: Based on Integrated Bioinformatic Analysis and Identification of Potential Biomarkers Most Related to Immunity

Background

The pathogenesis of ankylosing spondylitis (AS) is still not clear, and immune-related genes have not been systematically explored in AS. The purpose of this paper was to identify the potential early biomarkers most related to immunity in AS and develop a predictive disease risk model with bioinformatic methods and the Gene Expression Omnibus database (GEO) to improve diagnostic and therapeutic efficiency.

Methods

To identify differentially expressed genes and create a gene coexpression network between AS and healthy samples, we downloaded the AS-related datasets GSE25101 and GSE73754 from the GEO database and employed weighted gene coexpression network analysis (WGCNA). We used the GSVA, GSEABase, limma, ggpubr, and reshape2 packages to score immune data and investigated the links between immune cells and immunological functions by using single-sample gene set enrichment analysis (ssGSEA). The value of the core gene set and constructed model for early AS diagnosis was investigated by using receiver operating characteristic (ROC) curve analysis.

Results

Biological function and immune score analyses identified central genes related to immunity, key immune cells, key related pathways, gene modules, and the coexpression network in AS. Granulysin (GNLY), Granulysin (GZMK), CX3CR1, IL2RB, dysferlin (DYSF), and S100A12 may participate in AS development through NK cells, CD8⁺ T cells, Th1 cells, and other immune cells and represent potential biomarkers for the early diagnosis of AS occurrence and progression. Furthermore, the T cell coinhibitory pathway may be involved in AS pathogenesis.

Conclusion

The AS disease risk model constructed based on immune-related genes can guide clinical diagnosis and treatment and may help in the development of personalized immunotherapy.

EPSTI1 promotes monocyte adhesion to endothelial cells in vitro via upregulating VCAM-1 and ICAM-1 expression

Atherosclerosis is a chronic inflammatory disease of arterial wall, and circulating monocyte adhesion to endothelial cells is a crucial step in the pathogenesis of atherosclerosis. Epithelial-stromal interaction 1 (EPSTI1) is a novel gene, which is dramatically induced by epithelial-stromal interaction in human breast cancer. EPSTI1 expression is not only restricted to the breast but also in other normal tissues. In this study we investigated the role of EPSTI1 in monocyte-endothelial cell adhesion and its expression pattern in atherosclerotic plaques. We showed that EPSTI1 was dramatically upregulated in human and mouse atherosclerotic plaques when compared with normal arteries. In addition, the expression of EPSTI1 in endothelial cells of human and mouse atherosclerotic plaques is significantly higher than that of the normal arteries. Furthermore, we demonstrated that EPSTI1 promoted human monocytic THP-1 cell adhesion to human umbilical vein endothelial cells (HUVECs) via upregulating VCAM-1 and ICAM-1 expression in HUVECs. Treatment with LPS (100, 500, 1000 ng/mL) induced EPSTI1 expression in HUVECs at both mRNA and protein levels in a dose- and time-dependent manner. Knockdown of EPSTI1 significantly inhibited LPS-induced monocyte-endothelial cell adhesion via downregulation of VCAM-1 and ICAM-1. Moreover, we revealed that LPS induced EPSTI1 expression through p65 nuclear translocation. Thus, we conclude that EPSTI1 promotes THP-1 cell adhesion to endothelial cells by upregulating VCAM-1 and ICAM-1 expression, implying its potential role in the development of atherosclerosis.