Mutational Landscape of the Proglucagon-Derived Peptides

Abstract

Strong efforts have been placed on understanding the physiological roles and therapeutic potential of the proglucagon peptide hormones including glucagon, GLP-1 and GLP-2. However, little is known about the extent and magnitude of variability in the amino acid composition of the proglucagon precursor and its mature peptides. Here, we identified 184 unique missense variants in the human proglucagon gene GCG obtained from exome and whole-genome sequencing of more than 450,000 individuals across diverse sub-populations. This provides an unprecedented source of population-wide genetic variation data on missense mutations and insights into the evolutionary constraint spectrum of proglucagon-derived peptides. We show that the stereotypical peptides glucagon, GLP-1 and GLP-2 display fewer evolutionary alterations and are more likely to be functionally affected by genetic variation compared to the rest of the gene products. Elucidating the spectrum of genetic variations and estimating the impact of how a peptide variant may influence human physiology and pathophysiology through changes in ligand binding and/or receptor signalling, are vital and serve as the first important step in understanding variability in glucose homeostasis, amino acid metabolism, intestinal epithelial growth, bone strength, appetite regulation, and other key physiological parameters controlled by these hormones.

Keywords: GCG; GLP-1; GLP-2; GPCR; glucagon; mutant; pharmacogenomics; proglucagon.

Figure 1

Genetic variation of the Proglucagon peptide hormone gene. (A) Human germline genetic variation diversity of the proglucagon gene GCG located on chromosome (2q24.2) with expression mainly in the pancreas and the small intestine (see gtexportal.org for expression data). Cleavage enzymes differently cleave the proglucagon precursor into distinct peptides. (B) Cross-sectional mutational landscape aggregated from three independent genomic sequencing efforts including gnomAD [122,439 exomes/13,304 genomes, excluding individuals also found in TOPMed (12)], UK Biobank [200,629 exomes (13)], and TOPMed [132,345 genomes (14)] leading to a total set of 184 unique missense variants spanning 117 positions found in a total set of 468,717 unique individuals ( SI2 ). (C) Allele frequency (AF) spectrum of variants found in one, two, or all three cohorts respectively. The threshold for singletons, i.e., variants only carried by a single individual within a given cohort, are highlighted.

Figure 2

Proglucagon mutational landscape. Functionally described peptide hormones are highlighted (blue), which are more conserved in an evolutionary trace analysis employing Rate4Site scores [blue line, gaussian CI: 50% (46, 47)], than other parts of the precursor and peptides such as IP1/IP2, GRPP, and the signal peptide (most conserved position has a score of –1). All 184 missense variants are displayed along their predicted CADD scores (color-grading) (48) and their corresponding max allele frequencies (top-right y-axis). Peptide cleavage motifs are highlighted as grey bars alongside their known enzymes. Post-translational modification sites are highlighted (pink) in the amino acid sequence. Predicted CADD (purple) and primateAI (red) scores are presented (bottom curves) (48, 49), indicating higher mean predicted deleteriousness in glucagon and GLP-1. See SF1 for an interactive version.

Figure 3

Proglucagon peptide hormones are more conserved and more likely to be severely impacted by missense mutations observed in the human population. (A). Aggregated peptide mean evolutionary conservation scores from evolutionary trace Rate4Site scores, showing significant conservation of glucagon, GLP-1 and GLP-2 peptides (highlighted in blue). (B) Aggregated primeateAI scores across peptides, indicating higher predicted detrimental effects on glucagon, GLP-1 and GLP-2 peptide hormones. Mean difference analysis (bottom) from dabest (50) comparing other peptides to glucagon. P-values were calculated by a nonparametric Mann–Whitney test and distributions have been highlighted in green if below 0.05. See SI2 for more detailed information.

Figure 4

Proglucagon peptide hormone receptor structure and ligand-interaction with wild type (WT) or mutant genetic variant. (A, B) Predicted binding affinity changes (top panel) ΔΔG (kcal/mol) for genetic variations (glucagon: n=17; GLP-1: n=34) and alanine substitutions based on ΔΔG energy calculations on 10 independent runs from refined structure models (GLP-1 PDBid: 6X18; glucagon PDBid: 6LMK) (56). All genetic variants (marker: amino acid variant in bold) found in the combined set of genetic variant data ( Figure 1A ) and alanine substitutions for all positions in the respective receptor complexes (marker: A). Variants are colored based on different levels of energy, red (highly destabilizing), orange (destabilizing), yellow (slightly destabilizing), and grey (neutral). Evolutionary conservation on logo-plots (57) based on a multiple-sequence alignment (from Ensembl Compara (58),) of 222 high-confidence orthologues from 164 vertebrates (including in-paralogs). Each letter’s height represents the frequency within the aligned sequences ordered from the most conserved on the top of the letter stack. Polar contacts to the selected WT peptide position displayed in stick format. Peptide residues (light blue), genetic variant (orange), receptor residues (beige). Max fold change in IC₅₀ and EC₅₀ for the genetic variants presented with CADD score describing the genetic variant’s predicted deleteriousness. (A) Representation of glucagon-like peptide-1 receptor (GLP-1R) structure (grey) in complex with GLP-1^7-36-NH2 (blue) (PDBid: 6X18). (B) Representation of the glucagon receptor (GCGR) structure (grey) in complex with glucagon^1-29 (blue) (PDBid: 6LMK). See SI3 and SI4 for more information.