Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics

Abstract

Insulin is amongst the human genome’s most well-studied genes/proteins due to its connection to metabolic health. Within this article, we review literature and data to build a knowledge base of Insulin (INS) genetics that influence transcription, transcript processing, translation, hormone maturation, secretion, receptor binding, and metabolism while highlighting the future needs of insulin research. The INS gene region has 2076 unique variants from population genetics. 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 was confirmed within hundreds of pancreatic RNAseq samples, lacks drift based on human genome sequencing, and has possible elevated expression due to viral regulation within the liver. Moreover, a rare, poorly characterized African population-enriched variant of INS–IGF2 results in a loss of the stop codon. INS transcript UTR variants rs689 and rs3842753, associated with type 1 diabetes, are found in many pancreatic 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 that influence preproinsulin translation, proinsulin processing, dimer/hexamer secretory storage, receptor activation, and C-peptide detection for quasi-insulin blood measurements.

Gene Therapy for Genetic Syndromes: Understanding the Current State to Guide Future Care

“…Thus, it has become common that gene therapies are initiated only in individuals that have either a variant that occurs in multiple individuals (often autosomal recessive conditions) or the variant results in a frameshift or nonsense change that removes large chunks of protein observed to be removed in other patients with the disorder. There is a need for characterizing VUSs rapidly using existing data [ 23 , 64 , 65 , 66 , 67 , 68 ], high-throughput wet lab techniques used in NAA10 characterizations [ 69 ], knowledge from paralog proteins such as the work on SOX transcription factors [ 70 ], or through crowd-sourcing variant lists to identify matching variant locations and phenotypes as was the case for MED13 [ 71 ].…”