Understanding the role of GLUT2 in dysglycemia associated with Fanconi-Bickel syndrome (NanoString miRNA profiling)

Since the intronic mutation in SLC2A2 did not influence the activity of GLUT2, we undertook more investigation to understand the underlying molecular mechanisms of dysglycemia in FBS. We ran Nanostring miRNA panel v3b covering ~800 miRNAs in the patient, mother, and aged- and gender-matching healthy controls with no family history of dysglycemia. We noticed a higher degree of correlation between the patient and the gender and age-matched healthy control, rather than between the patient and the mother, suggesting that the miRNA expression profile might be more influenced by the age and gender rather than relatedness (Supplementary Fig. S5). The unsupervised hierarchical clustering revealed 123 miRs expressed specifically in the patient sample (Supplementary Table S2). The function of these miRNAs was interrogated by using Ingenuity Pathway (IPA) analysis software which returned 118 mapped miRs (Fig. 10). Here we report 30 miRNAs with the highest number of counts difference in the patient in comparison to controls (Fig. 11). We found that 14 of them were correlated with T1DM: 10 miRNAs (miR-199a, miR-25-3p, miR-93-5p, miR-19b-3p, miR-107, miR-24-3p. miR-18a-5p, miR-125b-5p, miR-324-5p, miR-331-3p, and hsa-miR-143-3p) were overexpressed in the control as compared to the patient, and 3 miRNAs (miR-144-3p , let-7e-5p, hsa-miR-29a-3p) were significantly overexpressed in the patient in comparison to the control. Molecular networks, including molecules inferred from previous studies, were generated by IPA functional analysis software (Supplementary Fig. S6). The molecular networks were given a score based on the number of molecules represented in the study dataset as compared to the literature. Network 1 (score 33) includes the genes and miRNAs implicated in organismal injury and abnormalities, skeletal and muscular system development and function, and tissue morphology. The miR-144 family integrated into the network 1. Network 2 (score 31) includes insulin and other genes and miRNAs implicated in glomerular injury, inflammatory disease, inflammatory response, and included miR-29 and let-7 families. These results suggest that dysglycemia in the patient with intronic mutation might be associated with the deregulation of miRNAs involved in insulin production and secretion in beta cells. The total RNA extracted from the patient-1 (db-bl-0008), her mother, and age- and gender-matched control-1 were submitted to the Omics Core at Sidra Medicine for Nanostring miRNA profiling. The Nanostring miRNA panel v3b (including ~800 targets) was run on all samples. A total of ~150 ng of total RNA was used as input for each of the samples assessed. Sample preparation, ligation, hybridization, detection, and scanning were performed as per the manufacturer's instructions. After hybridization, samples were transferred to the nCounter Prep Station, where excess probes were removed, and samples were aligned and immobilized on the nCounter cartridge. The cartridge was placed on the nCounter Digital Analyzer for data collection. The nSolver data analysis software (version 4.0 NanoString Technologies) was used for the assessment of QC and the normalization of the raw gene expression counts. We used the recommended default parameters for quality control flagging; briefly, flags were generated if samples did not meet the following QC criteria: imaging threshold with FOV registration of at least 75%, binding density between 0.05 and 2.25, positive control, and ligation control linearity with R2>0.95, positive control limit of detection 0.5fM, positive control > or = 2 standard deviations above the mean of the negative controls. Data are presented as normalized raw counts. Data was imported on ROSALIND (https://app.rosalind.bio/) and Partek Genomic Suite (Partek, St. Louis, Missouri, US) for secondary downstream analysis. Functional gene network analysis was performed using the Ingenuity Pathway Analysis system (QIAGEN, Hilden, Germany), which transforms large data sets into a group of relevant networks containing direct and indirect relationships between genes based on known interactions in the literature.