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

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 maturatio...

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Autores principales: Cook, Taylor W., Wilstermann, Amy M., Mitchell, Jackson T., Arnold, Nicholas E., Rajasekaran, Surender, Bupp, Caleb P., Prokop, Jeremy W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953665/
https://www.ncbi.nlm.nih.gov/pubmed/36830626
http://dx.doi.org/10.3390/biom13020257
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author Cook, Taylor W.
Wilstermann, Amy M.
Mitchell, Jackson T.
Arnold, Nicholas E.
Rajasekaran, Surender
Bupp, Caleb P.
Prokop, Jeremy W.
author_facet Cook, Taylor W.
Wilstermann, Amy M.
Mitchell, Jackson T.
Arnold, Nicholas E.
Rajasekaran, Surender
Bupp, Caleb P.
Prokop, Jeremy W.
author_sort Cook, Taylor W.
collection PubMed
description 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.
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spelling pubmed-99536652023-02-25 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. Biomolecules Review 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. MDPI 2023-01-30 /pmc/articles/PMC9953665/ /pubmed/36830626 http://dx.doi.org/10.3390/biom13020257 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Cook, Taylor W.
Wilstermann, Amy M.
Mitchell, Jackson T.
Arnold, Nicholas E.
Rajasekaran, Surender
Bupp, Caleb P.
Prokop, Jeremy W.
Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics
title Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics
title_full Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics
title_fullStr Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics
title_full_unstemmed Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics
title_short Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics
title_sort understanding insulin in the age of precision medicine and big data: under-explored nature of genomics
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953665/
https://www.ncbi.nlm.nih.gov/pubmed/36830626
http://dx.doi.org/10.3390/biom13020257
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