Matti Nykter (Tampere University)
Jake Lin, Elaheh Moradi, Karoliina Salenius, Suvi Lehtipuro, Tomi Hakkinen, Jutta E. Laiho, Sami Oikarinen , Sofia Randelin, Hemang Parikh, Jeffrey Krischer, Anette-G. Ziegler, Jorma Toppani, Ake Lernmark, Joe Petrosino, Nadim Ajami, Jin-Ziong She, Beena Akolkar, William A. Hagopian, Marian J. Rewers, Richard E. Lloyd, Kirsi Granberg, Heikki Hyoty, Matti Nykter
Genetic basis of type 1 diabetes has been characterized extensively and several non-genetic risk-modifying factors have been identified. While several studies have pointed out the role of environmental factors in the pathogenesis, possible contribution of host responses that are induced by these environmental factors is not known. The distinct genetic background of the two type 1 diabetes endotypes, that is children who initially develop autoantibodies against either insulin or GAD65, suggest that different gene-environment interactions may play a role in their pathogenesis. The purpose of this study was to elucidate these interactions in the emergence of the two divergent antibody patterns that predispose to type 1 diabetes.
We utilized 2376 matched longitudinal transcriptome sequencing data to characterize dynamic host responses during the prospective follow-up of children within the The Environmental Determinants of Diabetes in the Young (TEDDY) birth cohort study using nested case-control (NCC) design. Transcriptomics response profiles were analysed in children who later developed autoimmunity and in matched control children, and correlated them with IAA and GADA autoantibody patterns and with 4536 virome profiles in stool and plasma samples. DESeq2 and temporal filtering were applied for selecting of differentially expressed genes. Comprehensive immune cell type proportions were estimated by regression analysis with elastic net regularization. Conditional logistic regression was used to assess the associated odds of virome exposures, the expression of the selected genes, and cell type proportions to the IA outcome in the NCC setting.
Summary of Results
We identified distinct temporal gene expression patterns and proportions of immune cells in children with the first appearing autoantibody against either insulin or GAD65. Applying statistical testing between the cases and controls (adjusted p-value<0.05, LFC>0.5, 2+ timepoints), we identified 17 genes in GADA first and 3 genes in IAA first seroconversion cohorts. GAD65 linked genes include ZBED6, associated with pancreatic beta cell survival and FABP5, fatty acid gene modulating inflammation. The integration with enterovirus infections diagnosed by metagenomic sequencing data from stool and serum samples showed that the enterovirus-induced host response was weaker in children who later developed islet autoimmunity compared to enterovirus-infected autoantibody negative children exhibiting enriched type 1 interferon production (p-value<0.003), signaling (p-value<3.35e-11) and response (p-value< 0.0001). Case children who developed IAA as the first autoantibody had an elevated monocyte component, associated with chronic inflammation, throughout the time course. We showed that transcriptomic data provides additional independent information on top of genetic and environmental markers that can be used for improved islet autoimmunity prediction.Conclusions
In conclusion, our study showed immune related transcriptomic differences between cases and controls prior to islet autoimmunity. These are presented differently in children with the first appearing autoantibody against either insulin or GAD65. We also found that enterovirus infections lead to a stronger antiviral response in control children than in children who develop autoantibodies. A major strength of our study is the comprehensive integration of transcriptomic profiles, virome and genetics while incorporating immune cell type alterations prior to seroconversion. Taken together, our analysis provides transcriptomic and immunogenic characterization of host responses in the context of type 1 diabetes-related autoantibody patterns and environmental triggers of the disease.