Big data and genomics of Insulin to expand our knowledge

Abstract

Physiology research must be guided moving forward by our significant investments represented by deposited data. We hypothesize that integrating these resources, namely our investments into genomics, makes it possible to discover many aspects of well-studied physiological pathways. Within this work, we build a genomics integration for the Insulin gene/protein, which serves as an example of data integrations that can be applied to any physiological system. The INS gene region has 2,076 unique variants from population genetics of hundreds of thousands of individuals (gnomAD, ClinVar, Geno2MP, Avada, dbSNP). Several variants are found near the transcriptional start site, enhancers, and following the INS transcripts that might influence the readthrough fusion-transcript INS- IGF2. This INS- IGF2 transcript splice site is confirmed within hundreds of pancreas RNAseq samples, lacks drift based on human genome sequencing, and has a possible elevated expression due to viral regulation within the pancreas/liver. Moreover, a rare, poorly characterized African-specific variant of INS-IGF2 results in a loss of the stop codon. INS transcript UTR variants rs689 and rs3842753, highly associated with Type 1 Diabetes, are found in hundreds of pancreas RNAseq datasets with an elevation of the 3’UTR alternatively spliced INS transcript. Finally, by combining literature, evolutionary profiling, and structural biology, we map rare missense variants involved in type-1 diabetes, type-2 diabetes, and monogenic/neonatal diabetes that influence Pre-Pro-Insulin translation, Pro-Insulin processing, dimer/hexamer secretory storage, receptor activation, and C-peptide detection for quasi-Insulin blood measurements. Finally, through these complex Insulin pathways and multiple animal model species, we highlight the many genes involved and how environmental factors such as cellular stress may interact with genetics to increase the prevalence of diabetes. Overall, our data integration strategy yields a platform for merging our investments into animal physiological model systems, human clinical genomics, and growing investments into genome sequencing to highlight the many exciting future research needs of physiological pathways. This research was funded by the Gerber Foundation (to JWP), National Institutes of Health (K01ES025435 to JWP), and Michigan State University. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.