scholarly journals The non-obese diabetic mouse sequence, annotation and variation resource: an aid for investigating type 1 diabetes

Database ◽  
2013 ◽  
Vol 2013 ◽  
Author(s):  
Charles A. Steward ◽  
Jose M. Gonzalez ◽  
Steve Trevanion ◽  
Dan Sheppard ◽  
Giselle Kerry ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Diabetic Mouse ◽  
Sequence Annotation ◽  
Mouse Sequence

scholarly journals A Mimic of Viral Double-Stranded RNA Triggers Fulminant Type 1 Diabetes-like Syndrome in Regulatory T Cell-Deficient Autoimmune Diabetic Mouse

2011 ◽  
Vol 187 (10) ◽  
pp. 4947-4953 ◽  
Author(s):  
Ai Tada ◽  
Akira Shimada ◽  
Taketo Yamada ◽  
Yoichi Oikawa ◽  
Yoshifumi Yamada ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
T Cell ◽  
Regulatory T Cell ◽  
Diabetic Mouse ◽  
Fulminant Type 1 Diabetes ◽  
Double Stranded Rna ◽  
Fulminant Type

scholarly journals Genetic Interactions among Idd3, Idd5.1, Idd5.2, and Idd5.3 Protective Loci in the Nonobese Diabetic Mouse Model of Type 1 Diabetes

2013 ◽  
Vol 190 (7) ◽  
pp. 3109-3120 ◽  
Author(s):  
Xiaotian Lin ◽  
Emma E. Hamilton-Williams ◽  
Daniel B. Rainbow ◽  
Kara M. Hunter ◽  
Yang D. Dai ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Mouse Model ◽  
Diabetic Mouse ◽  
Genetic Interactions ◽  
Nonobese Diabetic Mouse ◽  
Nonobese Diabetic

The murine type 1 diabetes loci, Idd1, Idd3, Idd5, Idd9, and Idd17/10/18, do not control thymic CD4 − CD8 − /TCRαβ + deficiency in the nonobese diabetic mouse

2001 ◽  
Vol 12 (2) ◽  
pp. 175-176 ◽  
Author(s):  
Christopher J. Lord ◽  
Sarah Howlett ◽  
Paul A. Lyons ◽  
Laurence B. Peterson ◽  
Linda S. Wicker ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Diabetic Mouse ◽  
Nonobese Diabetic Mouse ◽  
Nonobese Diabetic

scholarly journals Thymically-derived Foxp3+ regulatory T cells are the primary regulators of type 1 diabetes in the non-obese diabetic mouse model

PLoS ONE ◽  
2019 ◽  
Vol 14 (10) ◽  
pp. e0217728 ◽  
Author(s):  
Daniel R. Holohan ◽  
Frédéric Van Gool ◽  
Jeffrey A. Bluestone
Keyword(s):  
T Cells ◽  
Type 1 Diabetes ◽  
Regulatory T Cells ◽  
Mouse Model ◽  
Diabetic Mouse

scholarly journals Consequences of Both Coxsackievirus B4 and Type 1 Diabetes on Female Non-Obese Diabetic Mouse Kidneys

2021 ◽  
Vol 9 (11) ◽  
pp. 2357
Author(s):  
Debra L. Walter ◽  
Jean R. Thuma ◽  
Ramiro Malgor ◽  
Frank L. Schwartz ◽  
Kelly D. McCall ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Kidney Disease ◽  
Disease Process ◽  
Kidney Injury ◽  
Nod Mice ◽  
The United States ◽  
Diabetic Mouse ◽  
Health Condition ◽  
Coxsackievirus B4

Despite the 2019 Executive Order on Advancing American Kidney Health Initiative, kidney disease has moved up in rank from the 9th to the 8th leading cause of death in the United States. A recent push in the field of nephrology has been to identify molecular markers and/or molecular profiles involved in kidney disease process or injury that can help identify the cause of injury and predict patient outcomes. While these studies have had moderate success, they have not yet considered that many of the health conditions that cause kidney disease (diabetes, hypertension, etc.) can also be caused by environmental factors (such as viruses), which in and of themselves can cause kidney disease. Thus, the goal of this study was to identify molecular and phenotypic profiles that can differentiate kidney injury caused by diabetes (a health condition resulting in kidney disease) and coxsackievirus B4 (CVB4) exposure (which can cause diabetes and/or kidney disease), both alone and together. Non-obese diabetic (NOD) mice were used for this study due to their susceptibility to both type 1 diabetes (T1D)- and CVB4-mediated kidney injury, in order to glean a better understanding of how hyperglycemia and viral exposure, when occurring on their own and in combination, may alter the kidneys’ molecular and phenotypic profiles. While no changes in kidney function were observed, molecular biomarkers of kidney injury were significantly up- and downregulated based on T1D and CVB4 exposure, both alone and together, but not in a predictable pattern. By combining individual biomarkers with function and phenotypic measurements (i.e., urinary albumin creatinine ratio, serum creatinine, kidney weight, and body weight), we were able to perform an unbiased separation of injury group based on the type of injury. This study provides evidence that unique kidney injury profiles within a kidney disease health condition are identifiable, and will help us to identify the causes of kidney injury in the future.

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