Characterization of Circulating Protein Profiles in Individuals with Prader-Willi Syndrome and Individuals with Non-Syndromic Obesity

Abstract

Background: Prader-Willi syndrome (PWS) is a rare genetic disorder characterized by distinctive physical, cognitive, and behavioral manifestations, coupled with profound alterations in appetite regulation, leading to severe obesity and metabolic dysregulation. These clinical features arise from disruptions in neurodevelopment and neuroendocrine regulation, yet the molecular intricacies of PWS remain incompletely understood. Methods: This study aimed to comprehensively profile circulating neuromodulatory factors in the serum of 53 subjects with PWS and 34 patients with non-syndromic obesity, utilizing a proximity extension assay with the Olink Target 96 neuro-exploratory and neurology panels. The ANOVA p-values were adjusted for multiple testing using the Benjamani-Hochberg method. Protein-protein interaction networks were generated in STRING V.12. Corrplots were calculated with R4.2.2 by using the Hmisc, Performance Analytics, and Corrplot packages Results: Our investigation explored the potential genetic underpinnings of the circulating protein signature observed in PWS, revealing intricate connections between genes in the PWS critical region and the identified circulating proteins associated with impaired oxytocin, NAD metabolism, and sex-related neuromuscular impairment involving, CD38, KYNU, NPM1, NMNAT1, WFIKKN1, and GDF-8/MSTN. The downregulation of CD38 in individuals with PWS (p < 0.01) indicates dysregulation of oxytocin release, implicating pathways associated with NAD metabolism in which KYNU and NMNAT1 are involved and significantly downregulated in PWS (p < 0.01 and p < 0.05, respectively). Sex-related differences in the circulatory levels of WFIKKN1 and GDF-8/MSTN (p < 0.05) were also observed. Conclusions: This study highlights potential circulating protein biomarkers associated with impaired oxytocin, NAD metabolism, and sex-related neuromuscular impairment in PWS individuals with potential clinical implications.

Keywords: Prader–Willi syndrome; circulating biomarkers; neuromodulatory factors; non-syndromic obesity; proteome.

Figure 1

Differences in the circulating protein biomarkers according to sex in the PWS subjects. Differences in the expression of protein markers are expressed as a normalized protein expression (NPX) unit. Values are presented as median with their respective interquartile ranges. Statistical significance levels are denoted as follows: *, p < 0.05; **, p < 0.01. DDR1: Epithelial discoidin domain-containing receptor 1; WFIKKN1: WAP Kazal immunoglobulin Kunitz and NTR domain-containing protein 1; GDF-8: Growth/Differentiation Factor 8, also known as myostatin; F: female (n = 29); M: male (n = 24).

Figure 2

Differences in the circulating protein biomarkers between the subjects with PWS and subjects with non-syndromic obesity. Differences in the expression of protein markers are expressed as a normalized protein expression (NPX) unit. Values are presented as median with their respective interquartile ranges. Statistical significance levels are as follows: *, p < 0.05; **, p < 0.01; ***, p < 0.001. PWS: subjects with Prader–Willi syndrome; OB: subjects with non-syndromic obesity.

Figure 2

Differences in the circulating protein biomarkers between the subjects with PWS and subjects with non-syndromic obesity. Differences in the expression of protein markers are expressed as a normalized protein expression (NPX) unit. Values are presented as median with their respective interquartile ranges. Statistical significance levels are as follows: *, p < 0.05; **, p < 0.01; ***, p < 0.001. PWS: subjects with Prader–Willi syndrome; OB: subjects with non-syndromic obesity.

Figure 3

Protein–protein interaction network. The network was constructed in STRING Version 12 by using a gene list consisting of the 29 circulating proteins identified through the Olink profiling and the proteins encoded by the genes present in the PWS-critical region on chromosome 15, including small nuclear ribonucleoprotein (SNRPN), MAGE family member L2 (MAGEL2), Necdin-MAGE Family Member (NDN), Makorin ring finger protein 3 (MKRN3), and Nuclear pore associated protein 1 (NPAP1). White nodes indicate the second shell of interactors. Blue and purple lines indicate known protein interactions from curated databases and experimentally validated interactions, respectively. Green, red, and deep blue lines indicate predicted interactions based on gene neighborhood, gene fusions, and gen co-occurrence, respectively. Lime green, black, and lilac indicate other types of connections represented by text mining, co-expression, or protein homology, respectively.

Figure 4

Clusterized correlogram of circulating protein candidates and clinical variables. Spearman’s correlation was used to determine the correlation coefficients. Unsupervised hierarchical clusterization was used to identify similar groups of correlating variables. The diameter and color depth of the dots are proportional to the p-value and correlation coefficient, respectively. Purple colors indicate a positive correlation while brown colors indicate a negative one. Only significant correlations were reported. (A) Correlation in PWS subjects. (B) Correlations in subjects with non-syndromic obesity.

Figure 4

Clusterized correlogram of circulating protein candidates and clinical variables. Spearman’s correlation was used to determine the correlation coefficients. Unsupervised hierarchical clusterization was used to identify similar groups of correlating variables. The diameter and color depth of the dots are proportional to the p-value and correlation coefficient, respectively. Purple colors indicate a positive correlation while brown colors indicate a negative one. Only significant correlations were reported. (A) Correlation in PWS subjects. (B) Correlations in subjects with non-syndromic obesity.