Deep analysis of skin molecular heterogeneities and their significance on the precise treatment of patients with psoriasis.

Background: Psoriasis is a highly heterogeneous autoinflammatory disease. At present, heterogeneity in disease has not been adequately translated into concrete treatment options. Our aim was to develop and verify a new stratification scheme that identifies the heterogeneity of psoriasis by the integration of large-scale transcriptomic profiles, thereby identifying patient subtypes and providing personalized treatment options whenever possible. Methods: We performed functional enrichment and network analysis of upregulated differentially expressed genes using microarray datasets of lesional and non-lesional skin samples from 250 psoriatic patients. Unsupervised clustering methods were used to identify the skin subtypes. Finally, an Xgboost classifier was utilized to predict the effects of methotrexate and commonly prescribed biologics on skin subtypes. Results: Based on the 163 upregulated differentially expressed genes, psoriasis patients were categorized into three subtypes (subtypes A-C). Immune cells and proinflammatory-related pathways were markedly activated in subtype A, named immune activation. Contrastingly, subtype C, named stroma proliferation, was enriched in integrated stroma cells and tissue proliferation-related signaling pathways. Subtype B was modestly activated in all the signaling pathways. Notably, subtypes A and B presented good responses to methotrexate and interleukin-12/23 inhibitors (ustekinumab) but inadequate responses to tumor necrosis factor-α inhibitors and interleukin-17A receptor inhibitors. Contrastly, subtype C exhibited excellent responses to tumor necrosis factor-α inhibitors (etanercept) and interleukin-17A receptor inhibitors (brodalumab) but not methotrexate and interleukin-12/23 inhibitors. Conclusions: Psoriasis patients can be assorted into three subtypes with different molecular and cellular characteristics based on the heterogeneity of the skin's immune cells and the stroma, determining the clinical responses of conventional therapies.

Deciphering genetic causality between inflammatory bowel disease and periodontitis through bi-directional two-sample Mendelian randomization.

Inflammatory bowel disease (IBD) and periodontitis are reported to be closely associated; however, whether there is a causal association between them remains unclear. To explore the existence of this causality, this study applied a bidirectional two-sample Mendelian randomization (MR). The genetic variants were obtained from the summary statistics of genome-wide association studies of IBD, including its subtypes CD and UC, and periodontitis. 175, 148, 113, and six single-nucleotide polymorphisms were selected as instrumental variables for IBD, CD, UC, and periodontitis, respectively. In MR analysis, random-effects inverse-variance weighted was used as the primary method, and weighted median and MR Egger regression were applied as the complementary method. A series of sensitivity analyses were also conducted to ensure the reliability of the results. None of these analyses found a significant effect of genetically proxied IBD and its subtypes on periodontitis, and vice versa. Subsequent sensitivity analyses did not detect any horizontal pleiotropy and heterogeneity. Caution should be exerted when it comes to clinical relevance and further studies are needed to clarify the relationship between IBD and periodontitis.

Mendelian randomization analysis suggests no associations of herpes simplex virus infections with systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) characterized by immune dysfunction is possibly more vulnerable to herpes simplex virus (HSV) infection. The infection has been intensively considered a common onset and exacerbation of SLE. This study is aimed at elucidating the causal association between SLE and HSV. A bidirectional two-sample Mendelian Randomization (TSMR) analysis was systematically conducted to explore the causal effect of SLE and HSV on each other. The causality was estimated by inverse variance weighted (IVW), MR-Egger and weighted median methods based on the summary-level genome-wide association studies (GWAS) data from a publicly available database. Genetically proxied HSV infection exhibited no causal association with SLE in the forward MR analysis using IVW method (odds ratio [OR] = 0.987; 95% confidence interval [CI]: 0.891-1.093; p = 0.798), nor did HSV-1 IgG (OR = 1.241; 95% CI: 0.874-1.762; p = 0.227) and HSV-2 IgG (OR = 0.934; 95% CI: 0.821-1.062; p = 0.297). Similar null results with HSV infection (OR = 1.021; 95% CI: 0.986-1.057; p = 0.245), HSV-1 IgG (OR = 1.003; 95% CI: 0.982-1.024; p = 0.788) and HSV-2 IgG (OR = 1.034; 95% CI: 0.991-1.080; p = 0.121) were observed in the reverse MR where SLE served as the exposure. Our study demonstrated no causal association between the genetically predicted HSV and SLE.

A Compendium of Mucosal Molecular Characteristics Provides Novel Perspectives on the Treatment of Ulcerative Colitis.

BACKGROUND AND AIMS: Ulcerative colitis [UC] is a complex heterogeneous disease. This study aims to reveal the underlying molecular features of UC using genome-scale transcriptomes of patients with UC, and to develop and validate a novel stratification scheme. METHODS: A normalised compendium was created using colon tissue samples (455 patients with UC and 147 healthy controls [HCs]), covering genes from 10 microarray datasets. Upregulated differentially expressed genes [DEGs] were subjected to functional network analysis, wherein samples were grouped using unsupervised clustering. Additionally, the robustness of subclustering was further assessed by two RNA sequencing datasets [100 patients with UC and 16 HCs]. Finally, the Xgboost classifier was applied to the independent datasets to evaluate the efficacy of different biologics in patients with UC. RESULTS: Based on 267 upregulated DEGs of the transcript profiles, UC patients were classified into three subtypes [subtypes A-C] with distinct molecular and cellular signatures. Epithelial activation-related pathways were significantly enriched in subtype A [named epithelial proliferation], whereas subtype C was characterised as the immune activation subtype with prominent immune cells and proinflammatory signatures. Subtype B [named mixed] was modestly activated in all the signalling pathways. Notably, subtype A showed a stronger association with the superior response of biologics such as golimumab, infliximab, vedolizumab, and ustekinumab compared with subtype C. CONCLUSIONS: We conducted a deep stratification of mucosal tissue using the most comprehensive microarray and RNA sequencing data, providing critical insights into pathophysiological features of UC, which could serve as a template for stratified treatment approaches.

Deep stratification by transcriptome molecular characters for precision treatment of patients with systemic lupus erythematosus.

OBJECTIVES: To leverage the high clinical heterogeneity of systemic lupus erythematosus (SLE), we developed and validated a new stratification scheme by integrating genome-scale transcriptomic profiles to identify patient subtypes sharing similar transcriptomic markers and drug targets. METHODS: A normalized compendium of transcription profiles was created from peripheral blood mononuclear cells (PBMCs) of 1046 SLE patients and 86 healthy controls (HCs), covering an intersection of 13 689 genes from six microarray datasets. Upregulated differentially expressed genes were subjected to functional and network analysis in which samples were grouped using unsupervised clustering to identify patient subtypes. Then, clustering stability was evaluated by the stratification of six integrated RNA-sequencing datasets using the same method. Finally, the Xgboost classifier was applied to the independent datasets to identify factors associated with treatment outcomes. RESULTS: Based on 278 upregulated DEGs of the transcript profiles, SLE patients were classified into three subtypes (subtype A-C) each with distinct molecular and cellular signatures. Neutrophil activation-related pathways were markedly activated in subtype A (named NE-driving), whereas lymphocyte and IFN-related pathways were more enriched in subtype B (IFN-driving). As the most severe subtype, subtype C [NE-IFN-dual-driving (Dual-driving)] shared functional mechanisms with both NE-driving and IFN-driving, which was closely associated with clinical features and could be used to predict the responses of treatment. CONCLUSION: We developed the largest cohesive SLE transcriptomic compendium for deep stratification using the most comprehensive microarray and RNA sequencing datasets to date. This result could guide future design of molecular diagnosis and the development of stratified therapy for SLE patients.

Specific enterotype of gut microbiota predicted clinical effect of methotrexate in patients with rheumatoid arthritis.

OBJECTIVE: The most used drug for the treatment of rheumatoid arthritis (RA) remains methotrexate (MTX). Unfortunately, up to 50% of patients do not achieve a clinically adequate outcome. Here we study whether the gut microbiota patterns can aid in the prediction of MTX efficacy for RA. METHOD: To dissect gut microbiome profiles of RA patients (n = 145), 16S rRNA gene sequencing was performed. Dirichlet multinomial mixture (DMM) clustering was used to identify enterotypes at genus level. The relationships between enterotypes and clinical measures (such as lymphocyte subsets and cytokines detected by flow cytometry) were explored. Then, enterotype stability was evaluated by the stratification of the RA patient cohort (n = 66) in Shanghai, China, using the same method. Finally, the enterotype-based gut microbial human index classifier was applied to another independent RA patient cohort (n = 27) to identify the factors associated with MTX clinical response. RESULTS: Our analysis revealed that the RA patients always displayed two different dysbiotic microbiota patterns: RA E1 comprised predominantly Prevotella and RA E2 comprised predominantly Bacteroides. Among all of the lymphocyte subsets and cytokines, only the number of CD8+ T cells showed a significant difference between RA E1 and RA E2. These results were validated in the RA patient cohort in Shanghai, China. Significant associations of RA E1 with clinical response to subsequent MTX treatment were confirmed by another independent RA patient cohort. CONCLUSION: Together, the enterotype-based gut microbial human index (EGMI) classifier was useful to precisely and effectively identify enterotypes of individual RA patients, which could effectively evaluate MTX clinical responses.

Predictive Value of Serum Lipoprotein-Associated Phospholipase A2 for Type 2 Diabetes Mellitus Complicated with Metabolic Syndrome in Elderly Patients.

BACKGROUND: The aim was to investigate the predictive value of serum lipoprotein-associated phospholipase A2 (Lp-PLA2) for type 2 diabetes mellitus (T2DM) complicated with metabolic syndrome (MS) in elderly patients. METHODS: A total of 296 patients with T2DM admitted from January 2019 to January 2021 were enrolled and assigned to MS group (n = 181) and non-MS group (n = 115). Their clinical data and laboratory test results were compared. Logistic regression analysis was employed to identify independent risk factors for MS in T2DM patients. Spearman's analysis was utilized to explore the correlations between serum Lp-PLA2 level and detection indicators. The predictive value of Lp-PLA2 for MS was analyzed by plotting receiver operating characteristic (ROC) curve, and Cox regression model was applied to explore the correlation of serum Lp-PLA2 level with MS. The results of data subjected to multivariate analysis were used to construct prediction models. RESULTS: The incidence rate of MS was 61.15% in T2DM patients. MS group had a significantly higher serum level of Lp-PLA2 than non-MS group (p < 0.05). Serum Lp-PLA2 was significantly positively correlated to FBG, glycosylated hemoglobin (HbA1c), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), FINS, and HOMA-IR, but significantly negatively associated with LDL-C (p < 0.05). The area under the ROC curve of Lp-PLA2 for predicting MS in T2DM patients was 0.724 (95% CI: 0.625 - 0.826, p < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value of Lp-PLA2 with an optimal cutoff value of 82.96 ng/mL were 73.7%, 85.4%, 77.56%, and 93.24%, respectively. TC, TG, HDL-C, HbA1c, and Lp-PLA2 were independent risk factors for MS (p < 0.05). The area under the ROC curve of the risk prediction model established based on these indicators was 0.823, and the cutoff value, Youden index, sensitivity, and specificity were 0.219, 0.656, 78.87%, and 87.66%, respectively, indicating higher predictive value. CONCLUSIONS: Increased serum Lp-PLA2 level is an independent risk factor for MS in T2DM patients. Lp-PLA2 (82.87 ng/mL) has high predictive value for MS.

Abnormalities of Peripheral Lymphocyte Subsets in Rheumatoid Arthritis Patients Complicated with Osteoporosis.

INTRODUCTION: Osteoporosis (OP) is one of the major comorbidities of rheumatoid arthritis (RA). Recent studies have shown that immune cells modulate bone health and regulate bone remodeling. However, the alterations of lymphocyte subsets in RA patients with OP are unclear. Here, we assessed the absolute numbers and proportions of the subsets in RA sufferers with OP and investigated the clinical significance. METHODS: A total of 777 RA patients and 117 gender- and age-matched healthy controls (HCs) were enrolled in this study. Patients were divided into RA-non-OP and RA-OP group according to their bone mineral density (BMD) and the history of fragility fracture. Peripheral lymphocyte subsets of participants were assessed by flow cytometry. RESULTS: Among 220 (28.31%) RA-OP patients, there were higher levels of erythrocyte sedimentation rate (ESR) (P = 0.011), C-reactive protein (CRP) (P = 0.028), rheumatoid factor (RF) (P = 0.013) and anti-cyclic citrullinated peptide antibody (ACPA) (P = 0.010), while red blood cells (RBC) (P = 0.039) were lower than those in RA-non-OP group. Compared with those of HCs and RA-non-OP group, the level of circulating Th17 cells in RA-OP patients was significantly increased (P < 0.05), while those of Tregs decreased (P < 0.01), leading to a higher ratio of Th17/Treg (P < 0.01). Notably, the level of B cells in both RA-non-OP and RA-OP group was reduced, this alteration was more obvious in patients with OP (P < 0.05). CONCLUSIONS: Immune disorders characterized by peripheral Th17/Treg imbalance and reduced B cells may contribute directly or indirectly to OP in RA, and this deserves more clinical attention.

Characteristics of the Gut Microbiome and Its Relationship With Peripheral CD4+ T Cell Subpopulations and Cytokines in Rheumatoid Arthritis.

This study investigated the association between intestinal microbiota abundance and diversity and cluster of differentiation (CD)4+ T cell subpopulations, cytokine levels, and disease activity in rheumatoid arthritis RA. A total of 108 rheumatoid arthritis (RA) patients and 99 healthy control (HC) subjects were recruited. PICRUSt2 was used for functional metagenomic predictions. Absolute counts of peripheral CD4+ T cell subpopulations and cytokine levels were detected by flow cytometry and with a cytokine bead array, respectively. Correlations were analyzed with the Spearman rank correlation test. The results showed that the diversity of intestinal microbiota was decreased in RA patients compared to HCs. At the phylum level, the abundance of Firmicutes, Fusobacteriota, and Bacteroidota was decreased while that of Actinobacteria and Proteobacteria was increased and at the genus level, the abundance of Faecalibacterium, Blautia, and Escherichia-Shigella was increased while that of Bacteroides and Coprococcus was decreased in RA patients compared to HC subjects. The linear discriminant analysis effect size indicated that Bifidobacterium was the most significant genus in RA. The most highly enriched Kyoto Encyclopedia of Genes and Genomes pathway in RA patients was amino acid metabolism. The relative abundance of Megamonas, Monoglobus, and Prevotella was positively correlated with CD4+ T cell counts and cytokine levels; and the relative numbers of regulatory T cells (Tregs) and T helper (Th17)/Treg ratio were negatively correlated with disease activity in RA. These results suggest that dysbiosis of certain bacterial lineages and alterations in gut microbiota metabolism lead to changes in the host immune profile that contribute to RA pathogenesis.

Development and validation of a GRGPI model for predicting the prognostic and treatment outcomes in head and neck squamous cell carcinoma.

Background: Head and neck squamous cell carcinoma (HNSCC) is among the most lethal and most prevalent malignant tumors. Glycolysis affects tumor growth, invasion, chemotherapy resistance, and the tumor microenvironment. Therefore, we aimed at identifying a glycolysis-related prognostic model for HNSCC and to analyze its relationship with tumor immune cell infiltrations. Methods: The mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA), while glycolysis-related genes were obtained from the Molecular Signature Database (MSigDB). Bioinformatics analysis included Univariate cox and least absolute shrinkage and selection operator (LASSO) analyses to select optimal prognosis-related genes for constructing glycolysis-related gene prognostic index(GRGPI), as well as a nomogram for overall survival (OS) evaluation. GRGPI was validated using the Gene Expression Omnibus (GEO) database. A predictive nomogram was established based on the stepwise multivariate regression model. The immune status of GRGPI-defined subgroups was analyzed, and high and low immune groups were characterized. Prognostic effects of immune checkpoint inhibitor (ICI) treatment and chemotherapy were investigated by Tumor Immune Dysfunction and Exclusion (TIDE) scores and half inhibitory concentration (IC50) value. Reverse transcription-quantitative PCR (RT-qPCR) was utilized to validate the model by analyzing the mRNA expression levels of the prognostic glycolysis-related genes in HNSCC tissues and adjacent non-tumorous tissues. Results: Five glycolysis-related genes were used to construct GRGPI. The GRGPI and the nomogram model exhibited robust validity in prognostic prediction. Clinical correlation analysis revealed positive correlations between the risk score used to construct the GRGPI model and the clinical stage. Immune checkpoint analysis revealed that the risk model was associated with immune checkpoint-related biomarkers. Immune microenvironment and immune status analysis exhibited a strong correlation between risk score and infiltrating immune cells. Gene set enrichment analysis (GSEA) pathway enrichment analysis showed typical immune pathways. Furthermore, the GRGPIdel showed excellent predictive performance in ICI treatment and drug sensitivity analysis. RT-qPCR showed that compared with adjacent non-tumorous tissues, the expressions of five genes were significantly up-regulated in HNSCC tissues. Conclusion: The model we constructed can not only be used as an important indicator for predicting the prognosis of patients but also had an important guiding role for clinical treatment.

The Gut Microbiota and Its Relevance to Peripheral Lymphocyte Subpopulations and Cytokines in Patients with Rheumatoid Arthritis.

Growing experimental and clinical evidence suggests that a chronic inflammatory response induced by gut microbiome critically contribute to the development of rheumatoid arthritis (RA). Previous studies demonstrated the disturbance of lymphocyte subpopulations in RA patients. The purpose of this study was to explore the characteristics of gut microbiome and the associations between bacterium and lymphocyte subpopulations as well as cytokines in patients with RA. Fecal samples from 205 RA patients and 199 healthy controls (HCs) were collected for bacterial DNA extraction and 16S ribosomal RNA (rRNA) gene sequencing. The levels of peripheral lymphocyte subpopulation such as T, B, CD4+T, CD8+T, NK, T helper 1 (Th1), Th2, Th17, and regulatory T cells (Tregs) of these subjects were detected by flow cytometry combined with standard absolute counting beads. The serum levels of cytokines interleukin-2 (IL-2), IL-4, IL-6, IL-10, IL-17, tumour necrosis factor-α (TNF-α), and interferon-γ (INF-γ) were tested by flow cytometric bead array (CBA). Alpha and beta diversity of gut microbiome were explored by bioinformatics analysis. Spearman rank correlation test was used to explore the relationships between gut microbiome and lymphocyte subsets as well as serum cytokines. The diversity and relative abundance of intestinal microbiota in patients with RA were significantly different from those in HCs. Detailly, the abundant of phylum Proteobacteria in RA patients was more than that in HCs, while Firmicutes was less than in HCs. There was increased relative abundance of genus Clostridium_XlVa as well as genus Blautia, more abundance of Ruminococcus2 in patients with lower levels of T, B, CD4+T, and Tregs. In addition, the relative abundances of Pelagibacterium, Oxalobacter, ClostridiumXlVb, and ClostridiumXVIII were correlated with cytokines. Gut microbiome of RA patients was clearly different from that of HCs. Abnormal bacteria communities are associated with the altered levels of lymphocyte subpopulation and cytokines, which might be one of the pathogenesis of RA.