RESUMO
Type 1 diabetes (T1D) is an autoimmune disease in which pancreatic islet ß-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.
Assuntos
Doenças Autoimunes , Diabetes Mellitus Tipo 1 , Feminino , Animais , Camundongos , Helicase IFIH1 Induzida por Interferon/genética , RNA Helicases DEAD-box/metabolismo , Linfócitos T CD8-Positivos/metabolismo , Predisposição Genética para Doença , Camundongos Endogâmicos NOD , Doenças Autoimunes/genética , Interferons/genéticaRESUMO
Type 1 diabetes (T1D) is an autoimmune disease in which pancreatic islet ß-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.
RESUMO
Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.