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

Cook, Taylor W.; Wilstermann, Amy M.; Mitchell, Jackson T.; Arnold, Nicholas E.; Rajasekaran, Surender; Bupp, Caleb P.; Prokop, Jeremy W.

doi:10.3390/biom13020257

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

by

Taylor W. Cook

^1,2,*,

Amy M. Wilstermann

³,

Jackson T. Mitchell

^1,2,

Nicholas E. Arnold

^1,2,

Surender Rajasekaran

^1,4,

Caleb P. Bupp

^1,5

and

Jeremy W. Prokop

^1,2,4,*

¹

Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA

²

Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, USA

³

Department of Biology, Calvin University, Grand Rapids, MI 49546, USA

⁴

Office of Research, Corewell Health, Grand Rapids, MI 49503, USA

⁵

Division of Medical Genetics, Corewell Health, Grand Rapids, MI 49503, USA

^*

Authors to whom correspondence should be addressed.

Biomolecules 2023, 13(2), 257; https://doi.org/10.3390/biom13020257

Submission received: 6 December 2022 / Revised: 20 January 2023 / Accepted: 26 January 2023 / Published: 30 January 2023

(This article belongs to the Special Issue Biosynthesis, Structure and Self-Assembly of Insulin)

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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.

Keywords: insulin; genomic variants; expression; splicing; protein folding; protein processing; receptor binding

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MDPI and ACS Style

Cook, T.W.; Wilstermann, A.M.; Mitchell, J.T.; Arnold, N.E.; Rajasekaran, S.; Bupp, C.P.; Prokop, J.W. Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics. Biomolecules 2023, 13, 257. https://doi.org/10.3390/biom13020257

AMA Style

Cook TW, Wilstermann AM, Mitchell JT, Arnold NE, Rajasekaran S, Bupp CP, Prokop JW. Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics. Biomolecules. 2023; 13(2):257. https://doi.org/10.3390/biom13020257

Chicago/Turabian Style

Cook, Taylor W., Amy M. Wilstermann, Jackson T. Mitchell, Nicholas E. Arnold, Surender Rajasekaran, Caleb P. Bupp, and Jeremy W. Prokop. 2023. "Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics" Biomolecules 13, no. 2: 257. https://doi.org/10.3390/biom13020257

APA Style

Cook, T. W., Wilstermann, A. M., Mitchell, J. T., Arnold, N. E., Rajasekaran, S., Bupp, C. P., & Prokop, J. W. (2023). Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics. Biomolecules, 13(2), 257. https://doi.org/10.3390/biom13020257

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

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