FCRL3 promotes IL-10 expression in B cells through the SHP-1 and p38 MAPK signaling pathways

Integrated analyses of single-cell transcriptome and Mendelian randomization reveal the protective role of FCRL3 in multiple sclerosis

Background

Multiple sclerosis (MS) represents a multifaceted autoimmune ailment, prompting the development and widespread utilization of numerous therapeutic interventions. However, extant medications for MS have proven inadequate in mitigating relapses and halting disease progression. Innovative drug targets for preventing multiple sclerosis are still required. The objective of this study is to discover novel therapeutic targets for MS by integrating single-cell transcriptomics and Mendelian randomization analysis.

Methods

The study integrated MS genome-wide association study (GWAS) data, single-cell transcriptomics (scRNA-seq), expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data for analysis and utilized two-sample Mendelian randomization study to comprehend the causal relationship between proteins and MS. Sequential analyses involving colocalization and Phenome-wide association studies (PheWAS) were conducted to validate the causal role of candidate genes.

Results

Following stringent quality control preprocessing of scRNA-seq data, 1,123 expression changes across seven peripheral cell types were identified. Among the seven most prevalent cell types, 97 genes exhibiting at least one eQTL were discerned. Examination of MR associations between 28 proteins with available index pQTL signals and the risk of MS outcomes was conducted. Co-localization analyses and PheWAS indicated that FCRL3 may exert influence on MS.

Conclusion

The integration of scRNA-seq and MR analysis facilitated the identification of potential therapeutic targets for MS. Notably, FCRL3, implicated in immune function, emerged as a significant drug target in the deCODE databases. This research underscores the importance of FCRL3 in MS therapy and advocates for further investigation and clinical trials targeting FCRL3.

Immune response stability to the SARS-CoV-2 mRNA vaccine booster is influenced by differential splicing of HLA genes

Many molecular mechanisms that lead to the host antibody response to COVID-19 vaccines remain largely unknown. In this study, we used serum antibody detection combined with whole blood RNA-based transcriptome analysis to investigate variability in vaccine response in healthy recipients of a booster (third) dose schedule of the mRNA BNT162b2 vaccine against COVID-19. The cohort was divided into two groups: (1) low-stable individuals, with antibody concentration anti-SARS-CoV IgG S1 below 0.4 percentile at 180 days after boosting vaccination; and (2) high-stable individuals, with antibody values greater than 0.6 percentile of the range in the same period (median 9525 [185-80,000] AU/mL). Differential gene expression, expressed single nucleotide variants and insertions/deletions, differential splicing events, and allelic imbalance were explored to broaden our understanding of the immune response sustenance. Our analysis revealed a differential expression of genes with immunological functions in individuals with low antibody titers, compared to those with higher antibody titers, underscoring the fundamental importance of the innate immune response for boosting immunity. Our findings also provide new insights into the determinants of the immune response variability to the SARS-CoV-2 mRNA vaccine booster, highlighting the significance of differential splicing regulatory mechanisms, mainly concerning HLA alleles, in delineating vaccine immunogenicity.

Identification of the immune-related biomarkers in Behcet's disease by plasma proteomic analysis

BACKGROUND

This study aimed to investigate the expression profile of immune response-related proteins of Behcet's disease (BD) patients and identify potential biomarkers for this disease.

METHODS

Plasma was collected from BD patients and healthy controls (HC). Immune response-related proteins were measured using the Olink Immune Response Panel. Differentially expressed proteins (DEPs) were used to construct prediction models via five machine learning algorithms: naive Bayes, support vector machine, extreme gradient boosting, random forest, and neural network. The prediction performance of the five models was assessed using the area under the curve (AUC) value, recall (sensitivity), specificity, precision, accuracy, F1 score, and residual distribution. Subtype analysis of BD was performed using the consensus clustering method.

RESULTS

Proteomics results showed 43 DEPs between BD patients and HC (P < 0.05). These DEPs were mainly involved in the Toll-like receptor 9 and NF-κB signaling pathways. Five models were constructed using DEPs [interleukin 10 (IL10), Fc receptor like 3 (FCRL3), Mannan-binding lectin serine peptidase 1 (MASP1), NF2, moesin-ezrin-radixin like (MERLIN) tumor suppressor (NF2), FAM3 metabolism regulating signaling molecule B (FAM3B), and O-6-methylguanine-DNA methyltransferase (MGMT)]. Among these models, the neural network model showed the best performance (AUC = 0.856, recall: 0.692, specificity: 0.857, precision: 0.900, accuracy: 0.750, F1 score: 0.783). BD patients were divided into two subtypes according to the consensus clustering method: one with high disease activity in association with higher expression of tripartite motif-containing 5 (TRIM5), SH2 domain-containing 1A (SH2D1A), phosphoinositide-3-kinase adaptor protein 1 (PIK3AP1), hematopoietic cell-specific Lyn substrate 1 (HCLS1), and DNA fragmentation factor subunit alpha (DFFA) and the other with low disease activity in association with higher expression of C-C motif chemokine ligand 11 (CCL11).

CONCLUSIONS

Our study not only revealed a distinctive immune response-related protein profile for BD but also showed that IL10, FCRL3, MASP1, NF2, FAM3B, and MGMT could serve as potential immune biomarkers for this disease. Additionally, a novel molecular disease classification model was constructed to identify subsets of BD.

Prioritization of Drug Targets for Neurodegenerative Diseases by Integrating Genetic and Proteomic Data From Brain and Blood

BACKGROUND

Neurodegenerative diseases are among the most prevalent and devastating neurological disorders, with few effective prevention and treatment strategies. We aimed to integrate genetic and proteomic data to prioritize drug targets for neurodegenerative diseases.

METHODS

We screened human proteomes through Mendelian randomization to identify causal mediators of Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, multiple sclerosis, frontotemporal dementia, and Lewy body dementia. For instruments, we used brain and blood protein quantitative trait loci identified from one genome-wide association study with 376 participants and another with 3301 participants, respectively. Causal associations were subsequently validated by sensitivity analyses and colocalization. The safety and druggability of identified targets were also evaluated.

RESULTS

Our analyses showed targeting BIN1, GRN, and RET levels in blood as well as ACE, ICA1L, MAP1S, SLC20A2, and TOM1L2 levels in brain might reduce Alzheimer's disease risk, while ICA1L, SLC20A2, and TOM1L2 were not recommended as prioritized drugs due to the identified potential side effects. Brain CD38, DGKQ, GPNMB, and SEC23IP were candidate targets for Parkinson's disease. Among them, GPNMB was the most promising target for Parkinson's disease with their causal relationship evidenced by studies on both brain and blood tissues. Interventions targeting FCRL3, LMAN2, and MAPK3 in blood and DHRS11, FAM120B, SHMT1, and TSFM in brain might affect multiple sclerosis risk. The risk of amyotrophic lateral sclerosis might be reduced by medications targeting DHRS11, PSMB3, SARM1, and SCFD1 in brain.

CONCLUSIONS

Our study prioritized 22 proteins as targets for neurodegenerative diseases and provided preliminary evidence for drug development. Further studies are warranted to validate these targets.

Identification of CD8+ T cell infiltration-related genes and their prognostic values in cervical cancer

Cervical cancer is a female-specific cancer with relatively high morbidity and mortality. As known to all, immune cell infiltrations in the cancer microenvironment are closely related to the cancer diagnosis and prognosis. Here we revealed that the CD8+ T cell infiltration was significantly upregulated in cervical cancer versus normal cervix uteri samples. Through univariate and multivariate cox analyses, we discovered that the CD8+ T cell infiltration was the only independent beneficial factor for the prognosis of cervical cancer. To explore the genes associated with the CD8+ T cell infiltration in cervical cancer, we performed the WGCNA analysis on the differentially expressed genes (DEGs) of cervical cancer versus normal cervix uteri tissues. As a result, 231 DEGs were found to be associated with CD8+ T cell infiltration in cervical cancer. Subsequently, with the Cytoscape analysis, we identified 105 hub genes out of the 231 DEGs. To further explore the genes that might be responsible for the prognosis of cervical cancer, we performed a univariate cox analysis followed by a LASSO assay on the 105 hub genes and located four genes (IGSF6, TLR10, FCRL3, and IFI30) finally. The four genes could be applied to the prediction of the prognosis of cervical cancer, and relatively higher expression of these four genes predicted a better prognosis. These findings contributed to our understanding of the prognostic values of CD8+ T cell infiltration and its associated genes in cervical cancer and thus might benefit future immune-related therapies.

TLR9/FCRL3 regulates B cell viability, apoptosis, and antibody and IL-10 production through ERK1/2, p38, and STAT3 signaling pathways

B cells play a role in the progression of multiple sclerosis (MS) and are closely related to Fc-receptor like-3 (FCRL3), but little is known about FCRL3 in B cells and MS. Activation of TLR9 in B cells with CpG found that CpG promoted FCRL3 expression in a dose- and time-dependent manner. CpG significantly activated ERK1/2, p38, and STAT3 pathways, and FCRL3 overexpression further promoted the activation of these pathways, while FCRL3 siRNA significantly inhibited the activation of these pathways by CpG. CpG stimulation significantly promoted the viability of B cells, inhibited cell apoptosis, and enhanced the production of antibodies and secretion of IL-10 by B cells. FCRL3 siRNA blocked most of the above regulatory effects of CpG, but promoted the further production of antibodies by B cells. FCRL3 overexpression enhanced the pro-survival, anti-apoptotic, and IL-10-inducing effects of CpG, but inhibited the effect of CpG on promoting antibody production. After adding inhibitors of ERK1/2, p38, and STAT3 pathways, respectively, the effects of CpG on promoting cell viability, antibody production, and IL-10 secretion were significantly reduced, but the anti-apoptotic effect of CpG was only affected by the blockade of STAT3 pathway. In addition, FCRL3 regulated B cell antibody and IL-10 secretion mainly through its ITIMs. These results indicate that TLR9 activation affects B cell proliferation, apoptosis, antibody production, and IL-10 secretion by upregulating FCRL3 expression, and is associated with ERK1/2, p38, and STAT3 pathways. Therefore, FCRL3 may be an important target for the diagnosis and treatment of B cell-related diseases.

Identification of Potential Prognostic Biomarkers Associated With Cancerometastasis in Skin Cutaneous Melanoma

Skin cutaneous melanoma (SKCM) is a highly aggressive tumor. The mortality and drug resistance among it are high. Thus, exploring predictive biomarkers for prognosis has become a priority. We aimed to find immune cell-based biomarkers for survival prediction. Here 321 genes were differentially expressed in immune-related groups after ESTIMATE analysis and differential analysis. Two hundred nineteen of them were associated with the metastasis of SKCM via weighted gene co-expression network analysis. Twenty-six genes in this module were hub genes. Twelve of the 26 genes were related to overall survival in SKCM patients. After a multivariable Cox regression analysis, we obtained six of these genes (PLA2G2D, IKZF3, MS4A1, ZC3H12D, FCRL3, and P2RY10) that were independent prognostic signatures, and a survival model of them performed excellent predictive efficacy. The results revealed several essential genes that may act as significant prognostic factors of SKCM, which could deepen our understanding of the metastatic mechanisms and improve cancer treatment.

Expression profile of Fc receptor-like molecules in patients with IgA nephropathy

BACKGROUND

Fc receptor-like (FCRL) molecules were considered to play a role in the pathogenesis of certain autoimmune diseases. Nonetheless, the clinical significance of FCRLs in IgA nephropathy (IgAN) remains unclear.

OBJECTIVE

This study is aimed at investigating the expression levels of FCRLs molecules in IgAN patients and determining its relevance to disease activity.

METHODS

The mRNA expression levels of FCRLs were determined in peripheral blood mononuclear cells (PBMCs) of 42 IgAN patients and 48 healthy controls by quantitative real-time PCR (qRT-PCR). FCRLs proteins expression in B cells of 25 IgAN patients, 14 patients with non-IgAN glomerulonephritis, and 29 healthy controls were detected by Flow cytometry. The Spearman correlation test was used to assess the correlation of FCRLs expression with clinical parameters of IgAN patients.

RESULTS

Our results indicated significant down-regulation of FCRL2 and FCRL3 mRNA levels in IgAN patients compared to healthy subjects. Surface protein expression of FCRLs molecules confirmed the qRT-PCR results. But FCRL2 and FCRL3 protein levels did not correlate with clinicopathologic phenotypes of IgAN patients. However, we found a significant positively correlation of FCRL2 and FCRL3 mRNA expression with the core 1 β1,3-galactosyltransferase (C1GALT1) and its molecular chaperone (Cosmc) mRNA levels in IgAN patients.

CONCLUSIONS

FCRL2 and FCRL3 expression levels in IgAN patients are significantly decreased and correlated with CIGALT1 and Cosmc mRNA expression.