Data integration and visualization in type 2 diabetes research and care: From biological mechanisms to precision medicine.

Summary, in English

Background. Type 2 diabetes (T2D) is a complex heterogeneous disorder affecting multiple organs. Gene expression studies in organs central to T2D, such as pancreatic islets and skeletal muscle will help elucidate and disentangle molecular mechanisms underlying T2D. Deep phenotyping using commonly measured clinical variables allows for the identification of clinically relevant T2D subgroups, representing a step towards individualized medicine. Creation and use of interactive, web-based tools can facilitate the application of these findings by researchers and clinicians.Aim. To analyze and investigate diabetes-related aberrations in gene expression in skeletal muscle and islets using bioinformatics approaches. To expand methodologies for diabetes sub-classification using anthropometric and biomarker data from T2D patients for clinical application. Toc reate and refine novel web applications providing unique resources for investigation of the metabolic abnormalities occurring in diabetes.
Methods. In paper I, curated, public skeletal muscle microarray gene expression data were used to analyze alterations in gene expression in relation to diabetes-related phenotypes such as BMI, T2D and age. In Paper II, RNA sequencing data on human islets from 188 donors were used to analyze the association between gene expression and diabetes status, BMI, and HbA1c, among other diabetes-related phenotypes. In paper III, a combined analysis of human islets from 420 donors were used to investigate the relationship between genetic variation and gene expression. In paper IV, a total of 43 samples from islet, liver, kidney and skin from human embryos/fetuses between 7-14 weeks post conception were sequenced using RNA sequencing. Gene expression of fetal tissues in relation to corresponding tissues in adults from GTEx were used to identify genes important in development. In paper V, clinical data including age of onset, sex, BMI, HbA1c, HOMA2IR, HOMA2B, and GAD antibodies collected at diagnosis from 8,406 T2D patients were used to implement a novel model of classification of T2D based on previously-published findings.
Results. In Paper I, phenotype data and gene expression data, as well as differential expression analyses of age, gender, diabetes, and acute training was used to create MuscleAtlasExplorer, an interactive data visualization web application providing complex functions for analysis of gene expression and phenotype data in skeletal muscle. In Paper II, a web application for gene look-ups, IsletGeneView, was created, providing gene-centric information for researchers with gene expression in human islets in relation to diabetes, HbA1c, BMI and co-expression with secretory genes, elucidating the complex phenotype-gene expression relationship in diabetic islets. In paper III, novel eQTLs in human islets were identified, increasing the insight into genetic variants influencing gene expression in human islets. In paper IV, FLEET was created, a web application atlas of gene expression in human fetal and adult islets, kidney, liver and skin. In paper V, a novel method for diabetes sub-classification with confidence scoring was devised and implemented. A novel software system for sub-classification of diabetes was created incorporating the sub-classification method, allowing for automatic classification of patients and entire diabetes research cohorts.
Conclusion. Taken together, this thesis shows the possibilities of using data visualization and web development to create resources for elucidating biological mechanisms underlying T2D. The resulting data is readily applicable and can be used to support functional studies. Furthermore, an automated system for diabetes sub-classification is presented and can facilitate support to clinicians, representing a first step towards individualized therapy.