CMTM7 plays key roles in TLR-induced plasma cell differentiation and p38 activation in murine B-1 B cells

Identification and validation of immune-related gene signature models for predicting prognosis and immunotherapy response in hepatocellular carcinoma

Background

This study seeks to enhance the accuracy and efficiency of clinical diagnosis and therapeutic decision-making in hepatocellular carcinoma (HCC), as well as to optimize the assessment of immunotherapy response.

Methods

A training set comprising 305 HCC cases was obtained from The Cancer Genome Atlas (TCGA) database. Initially, a screening process was undertaken to identify prognostically significant immune-related genes (IRGs), followed by the application of logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods for gene modeling. Subsequently, the final model was constructed using support vector machines-recursive feature elimination (SVM-RFE). Following model evaluation, quantitative polymerase chain reaction (qPCR) was employed to examine the gene expression profiles in tissue samples obtained from our cohort of 54 patients with HCC and an independent cohort of 231 patients, and the prognostic relevance of the model was substantiated. Thereafter, the association of the model with the immune responses was examined, and its predictive value regarding the efficacy of immunotherapy was corroborated through studies involving three cohorts undergoing immunotherapy. Finally, the study uncovered the potential mechanism by which the model contributed to prognosticating HCC outcomes and assessing immunotherapy effectiveness.

Results

SVM-RFE modeling was applied to develop an OS prognostic model based on six IRGs (CMTM7, HDAC1, HRAS, PSMD1, RAET1E, and TXLNA). The performance of the model was assessed by AUC values on the ROC curves, resulting in values of 0.83, 0.73, and 0.75 for the predictions at 1, 3, and 5 years, respectively. A marked difference in OS outcomes was noted when comparing the high-risk group (HRG) with the low-risk group (LRG), as demonstrated in both the initial training set (P <0.0001) and the subsequent validation cohort (P <0.0001). Additionally, the SVMRS in the HRG demonstrated a notable positive correlation with key immune checkpoint genes (CTLA-4, PD-1, and PD-L1). The results obtained from the examination of three cohorts undergoing immunotherapy affirmed the potential capability of this model in predicting immunotherapy effectiveness.

Conclusions

The HCC predictive model developed in this study, comprising six genes, demonstrates a robust capability to predict the OS of patients with HCC and immunotherapy effectiveness in tumor management.

Toll-like receptors 7 and 9 regulate the proliferation and differentiation of B cells in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune illness marked by the loss of immune tolerance and the production of autoantibodies against nucleic acids and other nuclear antigens (Ags). B lymphocytes are important in the immunopathogenesis of SLE. Multiple receptors control abnormal B-cell activation in SLE patients, including intrinsic Toll-like receptors (TLRs), B-cell receptors (BCRs), and cytokine receptors. The role of TLRs, notably TLR7 and TLR9, in the pathophysiology of SLE has been extensively explored in recent years. When endogenous or exogenous nucleic acid ligands are recognized by BCRs and internalized into B cells, they bind TLR7 or TLR9 to activate related signalling pathways and thus govern the proliferation and differentiation of B cells. Surprisingly, TLR7 and TLR9 appear to play opposing roles in SLE B cells, and the interaction between them is still poorly understood. In addition, other cells can enhance TLR signalling in B cells of SLE patients by releasing cytokines that accelerate the differentiation of B cells into plasma cells. Therefore, the delineation of how TLR7 and TLR9 regulate the abnormal activation of B cells in SLE may aid the understanding of the mechanisms of SLE and provide directions for TLR-targeted therapies for SLE.

Gene expression profile of placentomes and clinical parameters in the cows with retained placenta

BACKGROUND

Retained placenta (RP) is a prevalent disorder in cattle with many health-related and economic costs for the farm owners. Its etiology has not been clarified yet and there is no definite therapy for this disorder. In this study we conducted RNA-seq, hematologic and histologic experiments to survey the causes of RP development.

METHODS

Blood samples were collected from 4 RP and 3 healthy cows during periparturtion period for hematological assessments followed by placentome sampling within 30 min after parturition. Cows were grouped as RP and control in case the placenta was retained or otherwise expelled, respectively. Total RNA was extracted from placentome samples followed by RNA-sequencing.

RESULTS

We showed 240 differentially expressed genes (DEGs) between the RP and control groups. Enrichment analyzes indicated immune system and lipid metabolism as prominent over- and under-represented pathways in RP cows, respectively. Hormonal assessments showed that estradiol-17β (E2) was lower and cortisol tended to be higher in RP cows compared to controls at the day of parturition. Furthermore, histologic experiment showed that villi-crypt junctions remain tighter in RP cows compared to controls and the crypts layer seemed thicker in the placentome of RP cows. Complete blood cell (CBC) parameters were not significantly different between the two groups.

CONCLUSION

Overall, DEGs derived from expression profiling and these genes contributed to enrichment of immune and lipid metabolism pathways. We suggested that E2 could be involved in development of RP and the concentrations of P4 and CBC counts periparturition might not be a determining factor.

The Shaping of a B Cell Pool Maximally Responsive to Infections

B cell subsets differ in development, tissue distribution, and mechanisms of activation. In response to infections, however, all can differentiate into extrafollicular plasmablasts that rapidly provide highly protective antibodies, indicating that these plasmablasts are the main humoral immune response effectors. Yet, the effectiveness of this response type depends on the presence of antigen-specific precursors in the circulating mature B cell pool, a pool that is generated initially through the stochastic processes of B cell receptor assembly. Importantly, germinal centers then mold the repertoire of this B cell pool to be increasingly responsive to pathogens by generating a broad array of antimicrobial memory B cells that act as highly effective precursors of extrafollicular plasmablasts. Such B cell repertoire molding occurs in two ways: continuously via the chronic germinal centers of mucosal lymphoid tissues, driven by the presence of the microbiome, and via de novo generated germinal centers following acute infections. For effectively evaluating humoral immunity as a correlate of immune protection, it might be critical to measure memory B cell pools in addition to antibody titers.