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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-9953665 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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