Blood and CSF findings of cellular immunity in anti-NMDAR encephalitis.

OBJECTIVES: To investigate the immunopathogenic mechanisms of anti-N-methyl-D-aspartate receptor encephalitis (NMDAR-E) by characterizing the changes of immune cells in both peripheral blood (PB) and cerebrospinal fluid (CSF) of patients with NMDAR-E. METHODS: Cytology and flow cytometry were used to explore and compare different immunological parameters in PB and CSF of patients with NMDAR-E, viral encephalitis (VE) and healthy volunteers. Moreover, different models were established to assess the possibility of identifying NMDAR-E patients based on PB and CSF parameters. RESULTS: The neutrophil counts and monocyte-to-lymphocyte ratios (MLR) in PB are higher in NMDAR-E patients than in both VEs and controls (P < 0.001, respectively), while the percentages of CD3 + T, CD4 + T lymphocytes, and the leukocytes count in CSF were lower in NMDAR-Es than in VEs (P < 0.01, respectively). The higher percentages of CD8 + T cells in blood and CSF were both correlated with more severe NMDAR-E (P < 0.05, respectively). The poor neurological status group had significantly higher PB leukocytes but lower CSF leukocyte count (P < 0.05). Longitudinal observations in patients with NMDAR-E showed a decreasing trend of leukocyte count, neutrophils count, neutrophil-to-monocyte ratios (NMR), and neutrophil-to-lymphocyte ratios (NLR) with the gradual recovery of neurological function. CONCLUSIONS: The expression patterns of T lymphocyte subsets were different in patients with NMDAR-E and viral encephalitis. The changing trends of leukocyte and lymphocyte populations in peripheral blood and cerebrospinal fluid may provide clues for the diagnosis of different types of encephalitides, including NMDARE, and can be used as immunological markers to assess and predict the prognosis.

Identification of a novel peripheral blood signature diagnosing subclinical acute rejection after renal transplantation.

Background: Subclinical acute rejection (subAR) can only be diagnosed by protocol biopsy and is correlated with worse graft outcomes. However, noninvasive biomarkers of subAR are lacked for kidney transplantation recipients in clinic. This study aims to utilize to construct a peripheral blood-based gene signature for subAR diagnosis after kidney transplantation. Methods: After systematically screening databases, two cohorts of high quality with 3-month blood profiles and biopsy-proven graft status from the Gene Expression Omnibus databases were employed as training and validation cohorts. Then, the support vector machine recursive feature elimination (SVM-RFE) and the least absolute shrinkage and selection operator (LASSO) logistic regression were used to identify key biomarkers for subAR. Subsequently, the stepwise logistic regression method was applied to construct a gene signature for subAR in the training cohort. Patients were divided into high-risk and low-risk groups based on the cutoff point identified by the receiver operating characteristic (ROC) curve. Then, the signature was validated in a validation cohort with fixed formula. The single-sample gene set enrichment analysis was used to estimate immune cells in the blood. Results: Fifty key biomarkers were filtered out with the machine learning algorithms. Then, a novel six-gene signature was constructed using the LASSO and stepwise logistic regression method. The signature had high accuracy in both training [area under the curve (AUC) =0.923] and validation cohort (AUC =0.855). Additionally, these six genes were found to have significant and consistent relationships with blood immune cells in both cohorts, especially for T cells subtypes. Conclusions: We developed and validated a novel noninvasive six-gene signature based on peripheral blood to diagnose subAR, which offered a potential tool for clinical practice. The six-gene signature offered a potential method to monitor patients following transplantation and make a timely intervention.

A Three-Gene Peripheral Blood Potential Diagnosis Signature for Acute Rejection in Renal Transplantation.

Background: Acute rejection (AR) remains a major issue that negatively impacts long-term allograft survival in renal transplantation. The current study aims to apply machine learning methods to develop a non-invasive diagnostic test for AR based on gene signature in peripheral blood. Methods: We collected blood gene expression profiles of 251 renal transplant patients with biopsy-proven renal status from three independent cohorts in the Gene Expression Omnibus database. After differential expression analysis and machine learning algorithms, selected biomarkers were applied to the least absolute shrinkage and selection operator (LASSO) logistic regression to construct a diagnostic model in the training cohort. The diagnostic ability of the model was further tested in validation cohorts. Gene set enrichment analysis and immune cell assessment were also conducted for further investigation. Results: A novel diagnostic model based on three genes (TSEN15, CAPRIN1 and PRR34-AS1) was constructed in the training cohort (AUC = 0.968) and successfully verified in the validation cohort (AUC = 0.925) with high accuracy. Moreover, the diagnostic model also showed a promising value in discriminating T cell-mediated rejection (TCMR) (AUC = 0.786). Functional enrichment analysis and immune cell evaluation demonstrated that the AR model was significantly correlated with adaptive immunity, especially T cell subsets and dendritic cells. Conclusion: We identified and validated a novel three-gene diagnostic model with high accuracy for AR in renal transplant patients, and the model also performed well in distinguishing TCMR. The current study provided a promising tool to be used as a precise and cost-effective non-invasive test in clinical practice.

Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. METHODS: In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients. RESULTS: We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels. CONCLUSION: Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

Skin Transplantation and Lymphoid Organ Analysis in Mice.

Skin transplantation in mice is an important procedure to evaluate immune responses generated against heterologous grafts, especially given its highly immunogenic nature. In fact, skin is one of the most challenging organs in terms of allograft retention. In this protocol, we provide a detailed procedure for skin grafting using the tail skin as donor organ that is grafted on the dorsal site of thoracic cage in a recipient mouse. We also provide protocols for the systematic analysis of lymphoid organ analysis in transplanted mice. Together these protocols may be valuable for evaluation of parameters that affect skin grafting, including genetic factors, immune cell activation as well as the analysis of compounds that may be useful in allowing graft tolerance.

Citrobacter rodentium Infection Model for the Analysis of Bacterial Pathogenesis and Mucosal Immunology.

Citrobacter rodentium is a mouse restricted pathogen that was originally isolated from laboratory mouse colonies and causes transmissible colonic hyperplasia, characterized by thickening of the colon and inflammation. As a natural pathogen of mice, the infection model has proven critical to the development of our understanding of the pathogenesis of enteric disease and the mucosal immune response. In addition to this, some features of disease such as dysbiosis, inflammation, and wound healing replicate features of human inflammatory bowel diseases. As such, the C. rodentium infection model has become a key tool in investigations of many aspects of mucosal immunology.