Analysis of immune cell components and immune-related gene expression profiles in peripheral blood of patients with type 1 diabetes mellitus

The IFIH1-A946T risk variant promotes diabetes in a sex-dependent manner

Type 1 diabetes (T1D) is an autoimmune disease in which pancreatic islet β-cells are attacked by the immune system, resulting in insulin deficiency and hyperglycemia. One of the top non-synonymous single-nucleotide polymorphisms (SNP) associated with T1D is in the interferon-induced helicase C domain-containing protein 1 (IFIH1), which encodes an anti-viral cytosolic RNA sensor. This SNP results in an alanine to threonine substitution at amino acid 946 (IFIH1A946T) and confers an increased risk for several autoimmune diseases, including T1D. We hypothesized that the IFIH1A946T risk variant, (IFIH1R) would promote T1D pathogenesis by stimulating type I interferon (IFN I) signaling leading to immune cell alterations. To test this, we developed Ifih1R knock-in mice on the non-obese diabetic (NOD) mouse background, a spontaneous T1D model. Our results revealed a modest increase in diabetes incidence and insulitis in Ifih1R compared to non-risk Ifih1 (Ifih1NR) mice and a significant acceleration of diabetes onset in Ifih1R females. Ifih1R mice exhibited a significantly enhanced interferon stimulated gene (ISG) signature compared to Ifih1NR, indicative of increased IFN I signaling. Ifih1R mice exhibited an increased frequency of plasma cells as well as tissue-dependent changes in the frequency and activation of CD8+ T cells. Our results indicate that IFIH1R may contribute to T1D pathogenesis by altering the frequency and activation of immune cells. These findings advance our knowledge on the connection between the rs1990760 variant and T1D. Further, these data are the first to demonstrate effects of Ifih1R in NOD mice, which will be important to consider for the development of therapeutics for T1D.

The IFIH1-A946T risk variant promotes diabetes in a sex-dependent manner

Type 1 diabetes (T1D) is an autoimmune disease in which pancreatic islet β-cells are attacked by the immune system, resulting in insulin deficiency and hyperglycemia. One of the top non-synonymous single-nucleotide polymorphisms (SNP) associated with T1D is in the interferon-induced helicase C domain-containing protein 1 ( IFIH1 ), which encodes an anti-viral cytosolic RNA sensor. This SNP results in an alanine to threonine substitution at amino acid 946 (IFIH1 A946T ) and confers an increased risk for several autoimmune diseases, including T1D. We hypothesized that the IFIH1 A946T risk variant, ( IFIH1 R ) would promote T1D pathogenesis by stimulating type I interferon (IFN I) signaling leading to immune cell alterations. To test this, we developed Ifih1 R knock-in mice on the non-obese diabetic (NOD) mouse background, a spontaneous T1D model. Our results revealed a modest increase in diabetes incidence and insulitis in Ifih1 R compared to non-risk Ifih1 ( Ifih1 NR ) mice and a significant acceleration of diabetes onset in Ifih1 R females. Ifih1 R mice exhibited a significantly enhanced interferon stimulated gene (ISG) signature compared to Ifih1 NR , indicative of increased IFN I signaling. Ifih1 R mice exhibited an increased frequency of plasma cells as well as tissue-dependent changes in the frequency and activation of CD8 + T cells. Our results indicate that IFIH1 R may contribute to T1D pathogenesis by altering the frequency and activation of immune cells. These findings advance our knowledge on the connection between the rs1990760 variant and T1D. Further, these data are the first to demonstrate effects of Ifih1 R in NOD mice, which will be important to consider for the development of therapeutics for T1D.

Identification and verification of characteristic differentially expressed ferroptosis-related genes in osteosarcoma using bioinformatics analysis

BACKGROUND

This study identified and verified the characteristic differentially expressed ferroptosis-related genes (CDEFRGs) in osteosarcoma (OS).

METHODS

We extracted ferroptosis-related genes (FRGs), identified differentially expressed FRGs (DEFRGs) in OS, and conducted correlation analysis between DEFRGs. Next, we conducted GO and KEGG analyses to explore the biological functions and pathways of DEFRGs. Furthermore, we used LASSO and SVM-RFE algorithms to screen CDEFRGs, and evaluated its accuracy in diagnosing OS through ROC curves. Then, we demonstrated the molecular function and pathway enrichment of CDEFRGs through GSEA analysis. In addition, we evaluated the differences in immune cell infiltration between OS and NC groups, as well as the correlation between CDEFRGs expressions and immune cell infiltrations. Finally, the expression of CDEFRGs was verified through qRT-PCR, western blotting, and immunohistochemistry experiments.

RESULTS

We identified 51 DEFRGs and the expression relationship between them. GO and KEGG analysis revealed their key functions and important pathways. Based on four CDEFRGs (PEX3, CPEB1, NOX1, and ALOX5), we built the OS diagnostic model, and verified its accuracy. GSEA analysis further revealed the important functions and pathways of CDEFRGs. In addition, there were differences in immune cell infiltration between OS group and NC group, and CDEFRGs showed significant correlation with certain infiltrating immune cells. Finally, we validated the differential expression levels of four CDEFRGs through external experiments.

CONCLUSIONS

This study has shed light on the molecular pathological mechanism of OS and has offered novel perspectives for the early diagnosis and immune-targeted therapy of OS patients.

Application of multiple machine learning approaches to determine key pyroptosis molecules in type 2 diabetes mellitus

Objective

Pyroptosis, a lytic and inflammatory programmed cell death, has been implicated in type 2 diabetes mellitus (T2DM) and its complications. Nonetheless, it remains elusive exactly which pyroptosis molecule exerts an essential role in T2DM, and this study aims to solve such issue.

Methods

Transcriptional profiling datasets of T2DM, i.e., GSE20966, GSE95849, and GSE26168, were acquired. Four machine learning models, namely, random forest, support vector machine, extreme gradient boosting, and generalized linear modeling, were built based on pyroptosis genes. A nomogram of key pyroptosis genes was also generated, and the clinical value was appraised via calibration curves and decision curve analysis. Immune infiltration was inferred utilizing CIBERSORT. Drug-druggable target relationships were acquired from the Drug Gene Interaction Database. Through WGCNA, key pyroptosis-relevant genes were selected.

Results

Most pyroptosis genes exhibited upregulation in T2DM relative to controls, indicating the activity of pyroptosis in T2DM. The SVM model composed of BAK1, CHMP2B, NLRP6, PLCG1, and TIRAP exhibited the best performance in T2DM diagnosis, with AUC = 1. The nomogram can predict the risk of T2DM for clinical practice. NK cells resting exhibited a lower abundance in T2DM versus normal specimens, with a higher abundance of neutrophils. NLRP6 was positively linked with neutrophils. Drugs (keracyanin, 9,10-phenanthrenequinone, diclofenac, phosphomethylphosphonic acid adenosyl ester, acetaminophen, cefixime, aspirin, ustekinumab) potentially targeted the key pyroptosis genes. Additionally, CHMP2B-relevant genes were determined.

Conclusion

Altogether, this work proposes the key pyroptosis genes in T2DM, which might become possible molecules for the management and treatment of T2DM and its complications.

Natural Killer Cells, as the Rising Point in Tissues, Are Forgotten in the Kidney

Natural killer (NK) cells are members of a rapidly expanding family of innate lymphoid cells (ILCs). NK cells play roles in the spleen, periphery, and in many tissues, such as the liver, uterine, lung, adipose, and so on. While the immunological functions of NK cells are well established in these organs, comparatively little is known about NK cells in the kidney. Our understanding of NK cells is rapidly rising, with more and more studies highlighting the functional significance of NK cells in different types of kidney diseases. Recent progress has been made in translating these findings to clinical diseases that occur in the kidney, with indications of subset-specific roles of NK cells in the kidney. For the development of targeted therapeutics to delay kidney disease progression, a better understanding of the NK cell with respect to the mechanisms of kidney diseases is necessary. In order to promote the targeted treatment ability of NK cells in clinical diseases, in this paper we demonstrate the roles that NK cells play in different organs, especially the functions of NK cells in the kidney.

Exercise-induced skeletal muscle angiogenesis: impact of age, sex, angiocrines and cellular mediators

Exercise-induced skeletal muscle angiogenesis is a well-known physiological adaptation that occurs in humans in response to exercise training and can lead to endurance performance benefits, as well as improvements in cardiovascular and skeletal tissue health. An increase in capillary density in skeletal muscle improves diffusive oxygen exchange and waste extraction, and thus greater fatigue resistance, which has application to athletes but also to the general population. Exercise-induced angiogenesis can significantly contribute to improvements in cardiovascular and metabolic health, such as the increase in muscle glucose uptake, important for the prevention of diabetes. Recently, our understanding of the mechanisms by which angiogenesis occurs with exercise has grown substantially. This review will detail the biochemical, cellular and biomechanical signals for exercise-induced skeletal muscle angiogenesis, including recent work on extracellular vesicles and circulating angiogenic cells. In addition, the influence of age, sex, exercise intensity/duration, as well as recent observations with the use of blood flow restricted exercise, will also be discussed in detail. This review will provide academics and practitioners with mechanistic and applied evidence for optimising training interventions to promote physical performance through manipulating capillarisation in skeletal muscle.

Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets

Background: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges. Methods: Here, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies. Results: Using proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using Ingenuity Pathway Analysis (IPA) software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the “golden ratio” of 38.2%:61.8%. Specifically, the Canonical Pathway and Diseases and Functions modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The Biomarker Prediction module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis. Conclusions: These preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited.

Analysis of immune related gene expression profiles and immune cell components in patients with Barrett esophagus

Barrett's esophagus (BE) is a well-known precancerous condition of esophageal adenocarcinoma. However, the immune cells and immune related genes involved in BE development and progression are not fully understood. Therefore, our study attempted to investigate the roles of immune cells and immune related genes in BE patients. The raw gene expression data were downloaded from the GEO database. The limma package in R was used to screen differentially expressed genes (DEGs). Then we performed the least absolute shrinkage and selection operator (LASSO) and random forest (RF) analyses to screen key genes. The proportion of infiltrated immune cells was evaluated using the CIBERSORT algorithm between BE and normal esophagus (NE) samples. The spearman index was used to show the correlations of immune genes and immune cells. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of key genes in BE. A total of 103 differentially expressed immune-related genes were identified between BE samples and normal samples. Then, 7 genes (CD1A, LTF, FABP4, PGC, TCF7L2, INSR,SEMA3C) were obtained after Lasso analysis and RF modeling. CIBERSORT analysis revealed that resting CD4 T memory cells and gamma delta T cells were present at significantly lower levels in BE samples. Moreover, plasma cell and regulatory T cells were present at significantly higher levels in BE samples than in NE samples. INSR had the highest AUC values in ROC analysis. We identified 7 immune related genes and 4 different immune cells in our study, that may play vital roles in the occurrence and development of BE. Our findings improve the understanding of the molecular mechanisms of BE.