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. 2023 May 9;24(10):8499.
doi: 10.3390/ijms24108499.

GPR19 Coordinates Multiple Molecular Aspects of Stress Responses Associated with the Aging Process

Stuart Maudsley  1 , Claudia Schrauwen  1 , İrem Harputluoğlu  1 , Deborah Walter  1 , Hanne Leysen  1 , Patricia McDonald  2   3
Affiliations

GPR19 Coordinates Multiple Molecular Aspects of Stress Responses Associated with the Aging Process

Stuart Maudsley et al. Int J Mol Sci. .

Abstract

G protein-coupled receptors (GPCRs) play a significant role in controlling biological paradigms such as aging and aging-related disease. We have previously identified receptor signaling systems that are specifically associated with controlling molecular pathologies associated with the aging process. Here, we have identified a pseudo-orphan GPCR, G protein-coupled receptor 19 (GPR19), that is sensitive to many molecular aspects of the aging process. Through an in-depth molecular investigation process that involved proteomic, molecular biological, and advanced informatic experimentation, this study found that the functionality of GPR19 is specifically linked to sensory, protective, and remedial signaling systems associated with aging-related pathology. This study suggests that the activity of this receptor may play a role in mitigating the effects of aging-related pathology by promoting protective and remedial signaling systems. GPR19 expression variation demonstrates variability in the molecular activity in this larger process. At low expression levels in HEK293 cells, GPR19 expression regulates signaling paradigms linked with stress responses and metabolic responses to these. At higher expression levels, GPR19 expression co-regulates systems involved in sensing and repairing DNA damage, while at the highest levels of GPR19 expression, a functional link to processes of cellular senescence is seen. In this manner, GPR19 may function as a coordinator of aging-associated metabolic dysfunction, stress response, DNA integrity management, and eventual senescence.

Keywords: DNA; GPR19; adiposity; aging; damage; longevity; metabolism; mitochondria; receptor; stress.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
GPR19 expression is altered in an accelerated aging murine model. Using transcriptome profiling, we investigated the expression levels of GPR19 in GIT2KO (GIT2−knockout) mice (n = 3): (A). GPR19 expression levels were elevated in both central nervous system tissues (cortex (ctx), hippocampus (hippo), and hypothalamus (hypo)), as well as peripheral tissues (pancreas (panc), and liver (liv)) compared to wild-type (WT) littermates (n = 3). GAPDH expression was employed as an expression control. (B) GPR19 expression alteration results were replicated using Western blotting; beta-actin (ACTB) loading control was used. Hence, GPR19 expression was potentiated in the GIT2KO mice (#1,2,3) compared to control wild-type (WT: #1,2,3) animals (n = 3). The significant alterations of GPR19 expression in the advanced aging model GIT2KO are represented in the associated histogram (C). Ectopic expression of an HA-epitope tagged human WT GPR19 clone in HEK293 cells was demonstrated using a cDNA transfection level series from 0.5, 1, 2,5, and 10 μg (D). Cells were lysed at 24 h post-transfection and HA-tagged GPR19 expression was confirmed with selective Western blot. The optimal time course of expression of the median expression level (i.e., 2 μg) was assessed and found to be optimal at the standard 24 h time period following cDNA transfection (E). Differential compartment protein extraction was performed (cytoplasmic = solid line; plasma membrane = dotted line; nucleus/organelles = dashed line) before untargeted proteomic expression analysis (red bar indicates protein expression increase while blue bars indicate protein expression decrease—compared to the calculated proteomic baseline) was made across the stated expression level series (0.5, 1, 2, 5, and 10 μg cDNA). To simplify the data analysis of this complex expression matrix, the mean protein expression values across the different extraction conditions were calculated (F). The associated heatmap key indicates the range of log2 transformed GPR19: mock expression ratios. These mean data were then subjected to signaling pathway analysis using a Kolmogorov–Smirnoff (KS) test applied to the MSigDB Curated Reactome Database. One of the most prominent pathways populated by the total cellular perturbation response (from mean expression response data in panel (F)) to GPR19 expression was the Reactome “Cellular Response to Stress” (G). The yellow inset in panel G indicates the “leading edge” set of proteins that are responding to the positive KS score for “Cellular Response to Stress.” Extracting this protein set and then analyzing it for Gene Ontology Biological Process (GOBP) enrichment analysis revealed a strong phenotype for DNA-based stress management (H). Histogram-based data shown represent the means ± SEM (standard error of the mean). The significance level is indicated in each figure as * p ≤ 0.05; ** p ≤ 0.01.
Figure 2
Figure 2
GPR19 molecular signature is associated with DNA and energy management pathways. Using a specific MSigDB C2−Chemical and Genetic and Perturbations (CGP) database that encompasses complex molecular collections of proteins associated with well-characterized specific processes, we found that the GPR19 molecular perturbation signature most closely matched the PUJANA BRCA1 PCC Network. This suggests a strong potential functional relationship between GPR19 and BRCA1-related activity (A). Using a similarity clustering process with GeneTrail v3.2 (https://genetrail.bioinf.uni-sb.de/: accessed on 21 February 2023) a strong local MSigDB-CGP clustering between PUJANA BRCA1 PCC NETWORK, PUJANA CHEK2 PCC NETWORK, and the PUJANA ATM PCC NETWORK was found (B).
Figure 3
Figure 3
The GPR19 energy and DNA management signature is associated with oncological pathways. Using an unbiased metabolic pathways dataset (created using GLAD4U (http://glad4u.zhang-lab.org/index.php) GeneShot (https://maayanlab.cloud/geneshot/) and PubPular (https://heart.shinyapps.io/PubPular/), the protein identity overlap between the GPR19-BRCA1 (A)/ATM (B)/CHEK2 (C) MSigDB-CGP datasets and this metabolism dataset was found. The numerical extent of these specific dataset overlaps (black bars) was significantly greater than that expected from similarly sized random datasets (grey bars). STRING network analysis of the combined overlapping proteins (from the specific intersections in (A–C) revealed a potent multifactorial association of these DNA and energy management proteins with oncological pathways (D,E). The histogram in (F) depicts the most significantly populated NCBI-PubMed manuscript searches using the compounded data shown in the networks (D) (plain) and (E) (proteins color coded to the histogram bars in (F). Histogram-based data shown represent the means ± SEM (standard error of the mean). The significance level is indicated in each figure as ** p ≤ 0.01; *** p ≤ 0.001.
Figure 4
Figure 4
GPR19 expression level alteration reveals a progressive response system associated with aging-induced cellular pathology. The relative numbers of up− or down-regulated proteins significantly changed in cells in response to the dose-range of ectopic GPR19 expression (A). Protein expression bars indicated in red indicate upregulation in response to GPR19 expression; downregulated proteins are indicated by green bars. Applying Kolmogorov−Smirnoff-based pathway enrichment analysis to each of the distinct dose-dependent GPR19 response datasets revealed a strong phenotypic diversity in the molecular response to the GPR19 perturbation. Positively stimulated pathways (populated by upregulated proteins) are indicated in red, while negatively stimulated pathways (populated by downregulated proteins) are indicated in green. At the lowest expression level (0.5 μg–(B)), glucose and adipose regulatory pathways are dominant. At the next expression level (1 μg–(C)), ameliorative responses to oxidative stressors are observed. At the median expression level in the expression range (2 μg–(D)), the implication of GPR19 in oncological pathways is demonstrated. At the next level of GPR19 perturbagen expression (5 μg–(E)), DNA damage/telomere protective activity is observed. At the highest level of GPR19 expression (10 μg–(F)), responses to critical levels of cell stress are observed that may be linked to final cell fate decisions in the aging/damage process.
Figure 5
Figure 5
Consistent GPR19 energy-DNA management molecular signatures also occur in divergent protein extraction compartments. Separating GPR19 perturbagen datasets into cytoplasmic (solid line, (A−C), plasma membrane (dotted line, (D–F) or nuclear/organelle (dashed line, G–I), a commonly found core of proteins was identified (found at all levels of GPR19 perturbation) that when subjected to either KEGG (B,E,H) or Reactome (C,F,I) pathway analysis was again able to represent a pathway-based impression of energy and DNA integrity management. The associated heatmap key indicates the range of log2 transformed GPR19: mock expression ratios.
Figure 6
Figure 6
Extracting an MDC1−specific signature from the GPR19 perturbagen data corpus. Given the potential significance of MDC1 in the functional signature of GPR19, an unbiased MDC1 association network of the most proximal and significant one-hundred proteins was derived using STRING (A). This network was shown to be highly significant in its network characteristics compared to random data corpora of the same numerical size (B). Observing the intersection between proteins common to both the GPR19 perturbagen data set and the MDC1-specific network, we found that over a third of the MDC1 network (32 proteins) was significantly regulated by GPR19 perturbation (C). This level of overlap was significantly greater than that expected using a comparable-sized (to the actual MDC1 network) random dataset (C). The expression profile of GPR19−MDC1 common signature proteins (32 DEPs) is indicated in panel (D). The associated heatmap key indicates the range of log2 transformed GPR19: mock expression ratios. Histogram-based data shown represent the means ± SEM (standard error of the mean). The significance level is indicated in each figure as ** p ≤ 0.01; *** p ≤ 0.001.
Figure 7
Figure 7
The GPR19−MDC1 intersection data corpus encapsulates a microcosm of energy-DNA management functionality. A signaling-based minimal network (SIGNOR 2.0 Database—NetworkAnalyst: https://www.networkanalyst.ca/: accessed on 21 February 2023) was created for the GPR19-MDC1 32 protein intersection and was found to be centered upon many of the input thirty-two proteins (9/10 top network-controlling proteins were from the input thirty-two protein data corpus: (A). Applying KEGG signaling pathway annotation to the SIGNOR 2.0 network, an encapsulation of the overall GPR19 perturbagen phenotype was found, i.e., prominent pathways linked to cancer, DNA damage, and cell fate were defined. The signaling network was additionally found to be effectively linked to multiple proteins linked to classical signs of metabolic dysfunction. This indicates a novel functionality for GPR19−MDC1 (B). The proteins across the signaling network associated with the metabolic pathways are color coded on the network and correlate with the associated enrichment plot (B).
Figure 8
Figure 8
Protein subcomplex analysis of GPR19 immunoprecipitates. To assess the potential capacity for MDC1 and/or prohibitin (PHB)-associated complexes to link with GPR19, co-immunoprecipitation of MDC1, PHB, CBR1, and EIF4A1 with HA-epitope-tagged GPR19 was assessed in control and also aging-related stress conditions (AceK–Acesulfame K; low glucose; peroxide–hydrogen peroxide exposure) (A). Western blots indicate the specific protein presence in either the immunoprecipitated (IP) or the input whole cell lysate (w.c.l, 2% of total cell lysate loaded). Histograms indicating the mean of three co-immunoprecipitation replicates indicate stress-related potentiation of MDC1, PHB, and CBR1 (but not EIF4A1) with GPR19 (B). Histogram-based data shown represent the means ± SEM (standard error of the mean). The significance level is indicated in each figure as * p ≤ 0.05; ** p ≤ 0.01. Pathway enrichment probability was calculated via over-representation analysis (ORA).
Figure 9
Figure 9
Rationalizing a potential MDC1-PHB co-complex. Using the curated physical protein-protein interaction database at BioGrid (https://thebiogrid.org/: accessed on 21 February 2023), an interaction data corpus was identified for MDC1 (404 interactors) and PHB (1096 interactors). The intersection between these two corpora represented 122 proteins (A). Using randomly generated protein lists to mimic either the MDC1 (B) or PHB (C) interactor data corpus, the statistical significance of the intersection between the real data (A) was demonstrated. This suggests that such an overlap underlies the biological likelihood of an important MDC1-PHB functionality. In addition, ten randomly chosen proteins were also assessed when their similarly sized (to the MDC1 BioGrid interactor dataset) STRING networks were intersected with the actual PHB interactor corpus (D). Using the mean of the actual random protein overlap with PHB (at the number (E) and percentage levels (F)), a statistically significant distinction between random proteins and MDC1 intersection with PHB was found. The significance level is indicated in each figure as *** p ≤ 0.001.
Figure 10
Figure 10
Unbiased definition of potential MDC1-PHB complexes. The tissue-independent functional association network linking MDC1 and PHB was assessed using connection dynamics across the curated tissues in the HumanBase database of interaction modules. Applying a maximum connection network at the highest confidence level, the minimal bridging network linking MDC1 to PHB was found for the 142 distinct tissue databases at HumanBase (A,B). Across the 142 tissues, 727 distinct total proteins were found, and their identification frequency (linking MDC1 to PHB) was calculated. Using class-based statistics upon these 727 proteins list, the proteins (21) with a statistically significant distinct frequency to the mean were identified (C). These twenty-one proteins have their identification frequencies indicated across the 142 distinct tissues represented.
Figure 11
Figure 11
Network analyses of MDC1−PHB associated bridging factors. These tissue-independent linking factors between MDC1 and PHB were clustered using STRING (A) and then annotated using enrichment with the MSigDB CGP database (http://www.gsea-msigdb.org/gsea/msigdb/human/genesets.jsp?collection=CGP: accessed on 21 February 2023). The proteins that enriched the CGP database linked to breast cancer (pink), aging (blue), and metabolism (orange) cover the entire tissue-independent MDC1−PHB connecting network (B). Separating these proteins into these distinct functional groups identified that all the proteins lie within the group of those related to breast cancer (C). The proteins also linked to aging/DNA damage, and metabolism form specific subsets within the breast cancer-related networks. Of the twenty-one significant MDC1−PHB tissue-independent bridging proteins, fourteen of these (D) were found to be significantly altered in the initial GPR19 perturbagen dataset. The associated heatmap key indicates the range of log2 transformed GPR19: mock expression ratios.
Figure 12
Figure 12
GPR19 expression elevation provides cell viability protection against aging-related stress. HEK293 cell viability was assessed using automated cell viability analysis (Luna II Automated Cell Counter) at 6, 12, 24, 48, and 72 h (h) post-transfection with the median expression level of GPR19 (2 μg), which represented a multidimensional functional capacity with respect to stress resistance. Control HEK293 cells (transfected with 2 μg of an empty vector) or cells transfected with GPR19 were then treated for the indicated time period with either camptothecin (CPT, 1 mM) or hydrogen peroxide (peroxide, 100 nM). At each time point, the effect of GPR19 expression upon control, CPT-treated, or peroxide-treated cell viability was measured. Histogram-based data shown represent the means ± SEM (standard error of the mean). The significance level is indicated in each figure as * p ≤ 0.05; ** p ≤ 0.01.
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