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

2021 ◽  
Author(s):  
Jian Lin ◽  
Yuanhua Lu ◽  
Bizhou Wang ◽  
Ping Jiao ◽  
Jie Ma
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Nk Cells ◽  
Immune Cells ◽  
Support Vector ◽  
Hub Genes ◽  
Immune Related Genes

Abstract Background Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease caused by severe loss of pancreatic β cells. Immune cells are key mediators of β cell destruction. This study attempted to investigate the role of immune cells and immune-related genes in the occurrence and development of T1DM. Methods The raw gene expression profile of the samples from 12 T1DM patients and 10 normal controls was obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by Limma package in R. The least absolute shrinkage and selection operator (LASSO) - support vector machines (SVM) were used to screen the hub genes. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. Correlation of the hub genes and immune cells was analyzed by Spearman, and gene-GO-BP and gene-pathway interaction networks were constructed by Cytoscape plug-in ClueGO. Receiver operating characteristic (ROC) curves were used to assess diagnostic value of genes in T1DM. Results The 50 immune-related DEGs were obtained between the T1DM and normal samples. Then, the 50 immune-related DEGs were further screened to obtain the 5 hub genes. CIBERSORT analysis revealed that the distribution of plasma cells, resting mast cells, resting NK cells and neutrophils had significant difference between T1DM and normal samples. Natural cytotoxicity triggering receptor 3 (NCR3) was significantly related to the activated NK cells, M0 macrophages, monocytes, resting NK cells, and resting memory CD4+ T cells. Moreover, tumor necrosis factor (TNF) was significantly associated with naive B cell and naive CD4+ T cell. NCR3 [Area under curve (AUC) = 0.918] possessed a higher accuracy than TNF (AUC = 0.763) in diagnosis of T1DM. Conclusions The immune-related genes (NCR3 and TNF) and immune cells (NK cells) may play a vital regulatory role in the occurrence and development of T1DM, which possibly provide new ideas and potential targets for the immunotherapy of diabetes mellitus (DM).

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

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jian Lin ◽  
Yuanhua Lu ◽  
Bizhou Wang ◽  
Ping Jiao ◽  
Jie Ma
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Nk Cells ◽  
Immune Cells ◽  
Support Vector ◽  
Hub Genes ◽  
Immune Related Genes

Abstract Background Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease caused by severe loss of pancreatic β cells. Immune cells are key mediators of β cell destruction. This study attempted to investigate the role of immune cells and immune-related genes in the occurrence and development of T1DM. Methods The raw gene expression profile of the samples from 12 T1DM patients and 10 normal controls was obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by Limma package in R. The least absolute shrinkage and selection operator (LASSO)—support vector machines (SVM) were used to screen the hub genes. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. Correlation of the hub genes and immune cells was analyzed by Spearman, and gene-GO-BP and gene-pathway interaction networks were constructed by Cytoscape plug-in ClueGO. Receiver operating characteristic (ROC) curves were used to assess diagnostic value of genes in T1DM. Results The 50 immune-related DEGs were obtained between the T1DM and normal samples. Then, the 50 immune-related DEGs were further screened to obtain the 5 hub genes. CIBERSORT analysis revealed that the distribution of plasma cells, resting mast cells, resting NK cells and neutrophils had significant difference between T1DM and normal samples. Natural cytotoxicity triggering receptor 3 (NCR3) was significantly related to the activated NK cells, M0 macrophages, monocytes, resting NK cells, and resting memory CD4+ T cells. Moreover, tumor necrosis factor (TNF) was significantly associated with naive B cell and naive CD4+ T cell. NCR3 [Area under curve (AUC) = 0.918] possessed a higher accuracy than TNF (AUC = 0.763) in diagnosis of T1DM. Conclusions The immune-related genes (NCR3 and TNF) and immune cells (NK cells) may play a vital regulatory role in the occurrence and development of T1DM, which possibly provide new ideas and potential targets for the immunotherapy of diabetes mellitus (DM).

scholarly journals A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus

2021 ◽  
Vol 11 (4) ◽  
pp. 1742
Author(s):  
Ignacio Rodríguez-Rodríguez ◽  
José-Víctor Rodríguez ◽  
Wai Lok Woo ◽  
Bo Wei ◽  
Domingo-Javier Pardo-Quiles
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 1 Diabetes ◽  
Feature Selection ◽  
Blood Glucose ◽  
Type 1 Diabetes Mellitus ◽  
Support Vector ◽  
Chronic Hyperglycemia ◽  
Predictive Algorithms

Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).

Reduced CD4+ T-cell-specific gene expression in human type 1 diabetes mellitus

2007 ◽  
Vol 28 (4) ◽  
pp. 177-187 ◽  
Author(s):  
Tihamer Orban ◽  
Janos Kis ◽  
Laszlo Szereday ◽  
Peter Engelmann ◽  
Klara Farkas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
T Cell ◽  
Type 1 Diabetes Mellitus ◽  
Cd4 T Cell ◽  
Specific Gene ◽  
Human Type ◽  
Human Type 1 Diabetes

Reduced CD4+ T Cell-specific Gene Expression in Human Type 1 Diabetes Mellitus

2007 ◽  
Vol 123 ◽  
pp. S26-S27
Author(s):  
Tihamer Orban ◽  
Janos Kis ◽  
Laszlo Szereday ◽  
Klara Farkas ◽  
Peter Engelmann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
T Cell ◽  
Type 1 Diabetes Mellitus ◽  
Cd4 T Cell ◽  
Specific Gene ◽  
Human Type ◽  
Human Type 1 Diabetes

The NF-κB, IRF3, and SAPK/JNK signaling cascades of animal immune cells and their role in the progress of type 1 diabetes mellitus

2014 ◽  
Vol 457 (1) ◽  
pp. 255-257 ◽  
Author(s):  
E. G. Novoselova ◽  
M. O. Khrenov ◽  
S. B. Parfenyuk ◽  
T. V. Novoselova ◽  
S. M. Lunin ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Immune Cells ◽  
Signaling Cascades ◽  
Jnk Signaling

scholarly journals STUDY OF APOPTOSIS OF IMMUNE CELLS IN TYPE 1 DIABETES MELLITUS

2017 ◽  
Vol 19 (5) ◽  
pp. 635-640
Author(s):  
L. M. Hovsepyan ◽  
G. S. Kazaryan ◽  
A. V. Zanginyan ◽  
A. A. Akopdzhanyan ◽  
G. V. Zakharyan ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Immune Cells

scholarly journals Influence of rs1292037 Genetic Variant on miR-21 Gene Expression in Patients with type 1 Diabetes Mellitus

2021 ◽  
Author(s):  
Reza Bayat ◽  
Zivar Salehi ◽  
Setila Dalili ◽  
Farbod Bahreini
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Expression Level ◽  
Rna Molecules ◽  
Control Subjects ◽  
Non Coding Rna ◽  
Gene Expression Alterations

Abstract BackgroundMicroRNAs (miRNAs) are small non-coding RNA molecules that play a pivotal role in the central dogma of molecular biology by regulating gene expression. Alterations in the expression pattern of miRNAs are seen to be linked with several human diseases including autoimmune diseases such as pediatric type 1 diabetes mellitus (T1DM). Single nucleotide polymorphism (SNP) of the miRNAs coding genes can influence pancreatic development and insulin secretion. We contemplated a relation between miR-21 expression level as well as miR-21 rs1292037 SNP and pediatric T1DM.ResultsThe heterozygous T/C genotype was seen to be more common amongst T1DM patients than amongst controls (OR = 2.74 (1.78-4.27), P<0.0001). The C allele was more frequent in patients than in control subjects (OR = 1.36 (1.03-0.8), P = 0.02). miR-21 expression was seen to be upregulated in patients compared to the controls by more than twofold (p<0.0001). In the study population, miR-21 was found to be significantly upregulated when carrying the T/C genotype. ConclusionsWe report that the miR-21 rs1292037 variant is related to T1DM. Our study also suggests that the miR-21 expression level is upregulated in T1DM patients compared to the control subjects.

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