ATIC, A Potential Drug Target for Constipation: Evidence from Mendelian Randomization Integrating GWAS and eQTL Data

doi:10.21203/rs.3.rs-5232612/v1

Introduction: Constipation is a prevalent chronic gastrointestinal disorder. Current treatments primarily focus on short-term relief, leaving significant challenges for achieving long-term improvement. Therefore, identifying novel therapeutic targets is crucial.

Material and methods: We employed Mendelian randomization (MR) analysis, using cis-expression quantitative trait locus (cis-eQTL) data from the eQTLGen Consortium and summary data for constipation (ncase = 15,902; ncontrol = 395,721). We performed a replication of the MR analysis using data from the FinnGen study (ncase = 44,590; ncontrol = 409,143). Multiple methods, including Bayesian colocalization, Summary-data Mendelian Randomization (SMR) analysis, heterogeneity testing, Heterogeneity in Dependent Instrument (HEIDI), and horizontal pleiotropy testing, were applied to rigorously validate these findings and assess the likelihood of shared causal variants.

Results: The primary MR analysis identified eight druggable genes significantly associated with constipation risk (P < 0.05/2534, FDR corrected), with ATIC and PPIL1 demonstrating consistent associations in replication cohorts (P < 0.05). Sensitivity analyses confirmed the robustness of these findings, with no indication of heterogeneity. Co-localization analysis suggested that ATIC is likely to share causal variants with constipation susceptibility (PP.H4 = 0.91). Subsequent SMR analysis further confirmed a significant association between ATIC and constipation (P_SMR = 8.76 × 10^-4), while passing the HEIDI test (P_HEIDI = 3.46 × 10^-1). ATIC was linked to a lower risk of constipation (OR = 0.855, 95% CI = 0.811 – 0.902, P_FDR = 1.86 × 10^-5).

Conclusions: Our study identified the ATIC gene as a highly promising drug target for reducing the risk of constipation, laying the groundwork for future drug development.

Constipation

Druggable genes

ATIC

Mendelian randomization

Constipation is a prevalent gastrointestinal disorder, particularly in its chronic form, with an estimated prevalence of approximately 15% among adults [1, 2]. The primary symptoms of constipation include difficulty in defecation and dry, hard stools [3]. Chronic constipation can severely affect a patient's quality of life. Evidence indicates that the prevalence of major depressive disorder is higher among individuals with constipation compared to those without [4]. Additionally, constipation imposes a substantial burden on society. In the United States, annual medical costs for treating constipation range from $1,912 to $7,522 per patient [5].

Constipation can be classified as either primary or secondary, often resulting from underlying diseases or as a side effect of certain medications [6]. Currently, the primary treatment for constipation involves the use of medications, including osmotic laxatives, stimulant laxatives, secretagogues, and 5-Hydroxytryptamine receptor 4 (5-HT4) receptor agonists [7]. Although many of the medications recommended by current guidelines are effective for the short-term treatment of constipation, achieving long-term improvement in patients remains a significant challenge [8, 9]. Therefore, identifying novel drug targets for the prevention and treatment of constipation is of critical importance.

Genome-wide association studies (GWAS) provide a framework for identifying relationships between genetic variations, such as single nucleotide polymorphisms (SNPs), and specific diseases [10]. Mendelian randomization (MR) utilizes genetic variants identified in GWAS as instrumental variables (IVs) to establish causal relationships between genetic predispositions and constipation, offering valuable insights into the role of hereditary factors in the development of this condition [11]. In drug-target MR analysis, expression quantitative trait loci (eQTLs)—SNPs associated with gene expression regulation—serve as IVs to evaluate the influence of genes that can be targeted by drugs [12, 13]. Cis-eQTLs, which are located in genomic regions proximal to the target gene, are often selected for further analysis owing to their strong association with gene expression [14]. This method, based on the random distribution of genetic variations, enables the investigation of the genetic framework underlying constipation and the impact of drug targets, while avoiding the confounding variables often present in observational studies [15, 16].

In this study, we applied drug-target MR to analyze summary GWAS data on constipation and cis-eQTLs of drug target genes. Our aim was to identify relevant genes and explore therapeutic targets that may aid in preventing or slowing the progression of the disease, thereby contributing to advancements in the treatment of constipation.

Study design

The research methodology is outlined in Figure 1. Initially, the MR approach was employed to explore the causal relationship between druggable genes in the blood and constipation, with the robustness of the MR analysis ensured through comprehensive sensitivity evaluations. For druggable genes with significant MR findings, we performed a Bayesian co-localization analysis to determine whether the cis-eQTL and constipation share the same causal variant [17]. After identifying pertinent genes, we further validated the credibility of the MR results through a summary data-based MR (SMR) analysis and the heterogeneity in dependent instruments (HEIDI) test [14, 18].

Data sources

The term "druggable genes" refers to genes that encode proteins with sequence and structural similarities to established drug targets. In this study, the set of druggable genes was derived from the work of Finan et al., which identified a total of 4,479 genes. Of these, 1,427 genes encode proteins that serve as approved or clinical-stage drug targets; 682 genes encode proteins that either interact with established drug molecules or share similarities with recognized drug targets; and 2,370 genes belong to essential druggable gene families or encode proteins with distant resemblance to known drug targets [19].

We acquired cis-eQTL data for 2,888 druggable genes expressed in blood from the eQTLGen Consortium. This dataset, derived from a cohort of 31,684 participants, predominantly of European descent, consolidates data from multiple studies. The eQTLGen Consortium provides high-quality, large-scale genetic information, enabling the investigation of gene expression regulation in blood [20].

Constipation data was extracted from the GWAS database. We utilized summary-level information from a cohort of 4,11,623 individuals of European ancestry, encompassing 24,176,599 SNPs [21]. Additionally, we accessed the most recent R11 GWAS summary data on constipation from the FinnGen database, which includes 44,590 cases of constipation and 4,09,143 controls for replication MR analysis (Table 1) [22].

Table 1. Details of the constipation GWAS data.

Disease	Sources	Cases	Controls	Population	NO SNPs
Constipation	IEU	15,902	395,721	European	24,176,599
Constipation	FinnGen (R11)	44,590	409,143	European	21,306,794

Abbreviations: IEU, Integrative Epidemiology Unit; FinnGen (R11): Latest release 11 of the FinnGen Study

IV selection

In this study, we acquired cis-eQTL data for drug target genes and meticulously selected IVs appropriate for MR analysis [13]. SNPs were required to meet the criterion of genome-wide significance (P < 5 × 10^-⁸). To minimize the effects of linkage disequilibrium, we applied a clumping algorithm with parameters of r² = 0.1 and a 10,000 kb distance [23, 24]. Subsequently, we utilized the PhenoScanner database (https://www.phenoscanner.medschl.cam.ac.uk) to exclude SNPs associated with confounding factors and constipation, thereby mitigating the influence of potential confounders [25]. However, the website is currently no longer accessible.

MR analysis

We conducted the MR analysis using the "TwoSampleMR" package (version 0.5.6) in the R software environment (version 4.3.2) [26]. In this study, the primary analytical approach for investigating the causal relationships between cis-eQTLs and constipation was the Inverse Variance Weighted (IVW) method. This method employs a weighted regression of SNP-specific Wald estimates, allowing for an accurate evaluation of causal effects on constipation. The IVW method served as a critical tool in our analysis, providing insights into the intricate relationships within our dataset [27, 28]. To assess heterogeneity among the SNPs, we used Cochran's Q statistic [29, 30]. Furthermore, we applied the MR-PRESSO method to detect outliers and minimize potential bias due to horizontal pleiotropy [31]. The presence of pleiotropy can undermine the reliability of MR analysis results.

When a gene exhibited significance in both the primary and replication MR analyses (P < 0.05), we proceeded with SMR analysis to further validate the MR findings. SMR integrates GWAS summary statistics with eQTLs to investigate potential causal relationships between genes and specific phenotypes, employing the HEIDI test to assess the reliability of the outcomes [14]. We conducted the SMR analysis and the HEIDI test using the SMR software (available at https://yanglab.westlake.edu.cn/software/smr/). Gene expression data were obtained from the Genotype-Tissue Expression (GTEx) database.

Co-localization analysis

For druggable genes that met the significance thresholds in both MR and SMR analyses, we conducted co-localization analysis using the "coloc" package in R (version 5.1.0.1) [17]. This method integrates data from multiple SNPs or other genetic variants to determine whether genes and diseases share the same genomic regions or potentially interact. We applied the default prior probabilities: P1 = 1.0 × 10^-⁴ for the probability that an SNP is linked to druggable gene expression, P2 = 1.0 × 10^-⁴ for its association with the outcome, and P12 = 1.0 × 10⁻⁵ for its involvement in both. The co-localization analysis produced posterior probabilities for five distinct scenarios: PP.H0—no association with either gene expression or the outcome; PP.H1—association with gene expression only; PP.H2—association with the outcome but not gene expression; PP.H3—association with both gene expression and the outcome, but through different causal variants; and PP.H4—shared causal variants influencing both gene expression and the outcome. A PPH4 value greater than 0.85 was considered strong evidence for co-localization, identifying genes as potential therapeutic targets for constipation. SNPs most relevant to the outcome were carefully included in the co-localization analysis.

A total of 2,534 druggable genes were identified for further analysis based on the criteria for selecting IVs. Detailed information on the selected IVs is provided in Supplementary Table 2.

Primary and replication MR analyses

In this study, we initially performed Mendelian randomization (MR) analysis using drug target gene data from the IEU database and summary statistics from genome-wide association studies (GWAS) on constipation. With a significance threshold set, we ultimately identified eight druggable genes associated with constipation (P < 0.05/2534, FDR adjusted) (Figures 2 and 3; Supplementary Table 3). Subsequently, we conducted a replication MR analysis using the latest R11 data from the FinnGen database, where the expression of two of these genes, ATIC and PPIL1, showed a significant association with susceptibility to constipation (P < 0.05) (Figure 4; Supplementary Table 4). The conclusions from both the primary and replication MR analyses were consistent, indicating that the expression of these two druggable genes is associated with a reduced risk of constipation.

Sensitivity analyses conducted on these two genes indicated that both passed pleiotropy tests and showed no evidence of potential heterogeneity (Supplementary Tables 3 and 4). These two drug target genes were included for further analysis.

Co-localization analysis

In our study, we performed co-localization analysis on the eight genes identified through MR as being linked to constipation. This analysis assessed the likelihood of shared causal variants between cis-expression quantitative trait loci (cis-eQTLs) and constipation outcomes. Our results suggest that susceptibility to constipation may share a causal variant with the ATIC gene (PP.H4 > 0.85), as presented in Table 2.

Table 2. Gene and outcome co-localization results.

Disease	Gene	nSNPs	PP.H0	PP.H1	PP.H2	PP.H3	PP.H4	Grade
Constipation	ATIC	13	0.00	0.09	0.00	0.00	0.91	Tier 1 target
Constipation	PPIL1	10	0.00	0.97	0.00	0.00	0.03	Tier 2 target

PP.H0–PP.H4 represents the posterior probabilities of different hypotheses.

PP.H4 > 0.85 represents a strong colocalization between gene expression and risk of constipation.

SMR analysis

Following the primary MR, replication MR, and co-localization analyses, we conducted SMR analysis for further investigation. The SMR findings indicated that the ATIC gene was significantly linked to constipation (P_SMR < 0.05). Additionally, the ATIC gene passed the HEIDI test (P_HEIDI > 0.05). Detailed results of the SMR analysis can be found in Table 3. Based on the findings from the MR, SMR, and co-localization analyses, ATIC has been identified as a potential therapeutic target for reducing the risk of constipation.

Table 3. SMR Analysis Results of the Druggable Gene and Constipation.

Gene	Probe ID	Top SNP	P_GWAS	P_SMR	P_HEIDI
ATIC	ENSG00000138363	rs4535042	1.24 × 10^-4	8.76 × 10^-4	3.46 × 10^-1

Abbreviations: GWAS, Genome-wide association studies; HEIDI, Heterogeneity in Dependent Instrument;SMR, Summary-data Mendelian Randomization.

To explore potential therapeutic targets for constipation, we performed primary and replication MR analyses on 2,534 druggable genes to evaluate their relationship with constipation. Through a series of sensitivity analyses and additional assessments, such as Cochrane's Q test, MR-Egger regression, SMR analysis, and co-localization analysis, we identified ATIC as the most promising candidate for therapeutic intervention in constipation. The identification of a shared causal variant between ATIC gene expression and constipation provides compelling evidence of ATIC's significant role in mitigating constipation risk. This finding is consistent with previous studies highlighting the importance of effective purine metabolism in maintaining gastrointestinal health [32, 33]. Additionally, it opens new avenues for developing therapeutic strategies targeting this pathway for constipation treatment.

ATIC encodes a bifunctional enzyme responsible for two essential steps in purine biosynthesis: the conversion of 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) to formyl-AICAR (FAICAR) and catalyzing the subsequent formation of inosine monophosphate (IMP) [34]. Dysregulation of the purine pathway has been linked to various diseases, including cancer and metabolic disorders [35, 36]. The protective effect of ATIC against constipation likely stems from its central role in ATP synthesis, which is crucial for smooth muscle contractions in the gastrointestinal tract [37–39]. Reduced ATP levels can impair colonic motility, thereby increasing the risk of constipation.

Constipation is frequently associated with chronic low-grade inflammation, particularly in disorders such as irritable bowel syndrome (IBS) [40]. Inflammation in the enteric nervous system or gut smooth muscle can impair motility, leading to delayed transit times and hard stools [41, 42]. Research on purine metabolism indicates that AMP-activated protein kinase (AMPK), which is activated by AICAR (an intermediate in ATIC's pathway), plays a key role in regulating both energy homeostasis and inflammation [43]. The protective effect of ATIC against constipation may be associated with its ability to reduce intestinal inflammation. ATIC likely exerts anti-inflammatory effects by activating AMPK and inhibiting key inflammatory mediators such as NF-κB and IL-1β [44, 45], which could help alleviate symptoms in patients with constipation.

Notably, ATIC has been implicated not only in constipation but also in the development of hepatocellular carcinoma (HCC) [46]. The role of ATIC in HCC is primarily attributed to its critical function in purine biosynthesis, a pathway essential for the rapid proliferation of cancer cells [47]. This dual function underscores the complex biological roles of ATIC. In rapidly dividing tumor cells, it may promote cancer growth, whereas in healthy intestinal smooth muscle cells, it helps maintain normal cellular function and energy supply. This contrast highlights the tissue-specific and context-dependent actions of ATIC, suggesting the need for further research into its relationship with HCC.

To date, studies directly linking ATIC to constipation are limited. However, the MR approach offers a distinct advantage in exploring genetic factors associated with complex diseases. Our research provides compelling evidence supporting a protective role of ATIC in constipation, marking the first time ATIC has been proposed as a potential therapeutic target for this condition.

Despite the strengths of this study, certain limitations must be acknowledged. The drug-target MR approach assumes idealized conditions, such as prolonged, low-dose drug exposure. However, real-world environments are more complex, with various external factors potentially introducing confounding variables. Consequently, MR analysis cannot fully replace clinical trials, and the therapeutic efficacy of ATIC remains to be confirmed. Furthermore, MR assesses the impact of single druggable gene expression on outcomes in isolation, whereas many drugs exert their effects through interactions with multiple molecular targets. Finally, although we used several GWAS datasets, most participants were of European descent, which may limit the generalizability of our findings to other ethnic groups.

Drug-target MR offers a novel approach for identifying potential therapeutic targets by leveraging genetic data associated with druggable genes in conjunction with disease-specific GWAS results. In this study, we integrated data from various databases and employed advanced techniques, such as MR, SMR, and co-localization analyses, to identify key druggable genes linked to constipation. Notably, our results highlighted the protective role of ATIC in reducing the risk of constipation. This critical discovery not only positions ATIC as a promising target for the development of more effective biomarkers and treatments but also advances our understanding of the molecular mechanisms underlying constipation. Nevertheless, extensive experimental and clinical investigations are required to validate and confirm the role of ATIC in constipation prevention and treatment.

Ethics approval and consent to participate

Ethics approval was not applicable since the study is based on summary-level data. In all original studies, ethics approval and consent to participate was obtained.

Consent for publication

Not applicable.

Competing interests

The authors declare no potential competing interests.

Funding

This study received financial support from a research fund from the National Natural Science Foundation of China (82374454 and 82260938).

Availability of data and materials

This study involves publicly available summary data. The summary data for the genome-wide association study (GWAS) on constipation can be accessed at https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90018829/. The summary data for the replication MR analysis from the FinnGen study can be found at https://storage.googleapis.com/finngen-public-data-r11/summary_stats/finngen_R11_K11_CONSTIPATION.gz. The cis-eQTL data were obtained from the eQTLGen Consortium (https://www.eqtlgen.org/cis-eqtls.html), with detailed IDs provided in Supplementary Table 1.

Authors’ contributions

P.S. and Q.L. contributed equally to this work. P.S. and Q.L. were involved in the conception and design of the study, data analysis, and interpretation of results. Y.D. assisted with data collection and statistical analysis. W.G., W.L., C.L., and W.X. contributed to the methodology and provided critical insights throughout the research process. X.G. and J.W. aided in drafting the manuscript and revising it for important intellectual content. L.Z. coordinated the overall project, supervised the study, and is the corresponding author for this publication.

Acknowledgments

We extend our gratitude to those involved in database collation for enabling public access to the data, to the participants and investigators of the FinnGen study, and to Bullet Edits Limited for the linguistic editing and proofreading of our manuscript.

Declaration of generative AI and AI-assisted technologies in the writing process

In preparing this work, the authors utilized ChatGPT for translation and language polishing. Following its use, the authors thoroughly reviewed and revised the content, assuming full responsibility for the final published version.

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No competing interests reported.

SupplementaryTables.xlsx

ATIC, A Potential Drug Target for Constipation: Evidence from Mendelian Randomization Integrating GWAS and eQTL Data

Status:

Version 1

Abstract

Figures

Background

Methods

Study design

Data sources

MR analysis

Co-localization analysis

Results

Primary and replication MR analyses

Co-localization analysis

Discussion

Conclusions

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Funding

Availability of data and materials

Authors’ contributions

Acknowledgments

Declaration of generative AI and AI-assisted technologies in the writing process

References

Additional Declarations

Supplementary Files

Status:

Version 1