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Causal effects of serum metabolites on amyotrophic lateral sclerosis: A Mendelian randomization study
Progress in Neuropsychopharmacology & Biological Psychiatry 97 (2020) 109771
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Causal effects of serum metabolites on amyotrophic lateral sclerosis: A Mendelian randomization study Lihong Yanga, Xiaohong Lvb, Hanzhi Duc, Di Wuc, Mengchang Wangc,
T
⁎
a
Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China Department of Rheumatism and Immunology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China c Department of Hematopathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Serum metabolite Amyotrophic lateral sclerosis Mendelian randomization GSH metabolism dysfunction
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder that is affected by both genetic and environmental factors. Nowadays, OMIC technologies, such as genomics and metabolomics, are providing a systematic readout of genetic structures and physiological states for understanding human diseases. However, the comprehensive analysis of cross-omics is often lacking. Here, we conducted a Mendelian randomization analysis to provide a comprehensive analysis of metabolomics and genomics to estimate the causal relationships between non-targeted human serum metabolites and the development of ALS. Using genetic variants as predictors, our study detected 18 metabolites that might have causal effects on the development of ALS, including a group of gamma-glutamyl amino acids. Our findings suggested that glutathione metabolism dysfunction might be involved in the pathogenesis of ALS. Furthermore, our study provides a novel method to understand the pathogenesis of human diseases and develop therapeutic strategies for diseases by combining metabolomics with genomics.
1. Introduction Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease that is characterized by degeneration of both upper and lower motor neurons (Hardiman et al., 2017; Kiernan et al., 2011). The clinical symptoms begin insidiously with focal weakness, but spread relentlessly to most muscles, including the diaphragm (Brown and AlChalabi, 2017). ALS generally leads to the death of the patient within 2–5 years of appearance of the first symptoms, due to complications in respiratory functions (Costa and de Carvalho, 2016; de Carvalho et al., 1996). So far, there have been no effective treatments for patients with ALS, despite a large number of clinical trials. Extensive efforts have been made to identify novel biomarkers that could help in early diagnosis, understanding disease progression, and finding therapeutic targets for ALS (Kruger et al., 2013; Otto et al., 2012; Tarasiuk et al., 2012; Turner et al., 2013a, b). However, the progress so far has been modest in terms of understanding the pathophysiological mechanism underlying the development of ALS. Genetic predispositions and their interactions with environmental factors play a crucial role in the development of ALS(Riva et al., 2016).
Currently, OMIC technologies, such as genomics and metabolomics, are widely applied to identify potential markers that could help unravel pathophysiological mechanisms or therapeutic strategies for human diseases (Holmes et al., 2008). Genome wide association studies (GWAS) and metabolomics have identified many potential genetic variants and metabolites, respectively, that might contribute to the development of ALS (Fogh et al., 2014; Laaksovirta et al., 2010; Mitropoulos et al., 2018; van Es et al., 2009). However, these findings are often scattered and provide little information about the underlying biological processes of ALS. A comprehensive analysis is needed to understand the relationships and interactions between genetic variations and metabolic factors, and their potential roles in causing ALS. Recently, GWAS were extended to metabolic phenotypes which generated an atlas of genetically determined metabolites (GDMs) (Gieger et al., 2008; Kettunen et al., 2012; Shin et al., 2014; Suhre et al., 2011). The GDMs constitute functional intermediates that reveal the role of the interactions between genetic variants and metabolites in the pathogenesis of human diseases. Mendelian randomization (MR) is a novel technique that uses genetic variants as instrumental variables to assess the causality of an
Abbreviations: ALS, Amyotrophic lateral sclerosis; GWAS, Genome wide association studies; GDM, Genetically determined metabolites; GSH, glutathione; MR, Mendelian randomization; SNP, Single nucleotide polymorphism; IVW, Inverse variance weighted ⁎ Corresponding author. E-mail address: [email protected] (M. Wang). https://doi.org/10.1016/j.pnpbp.2019.109771 Received 5 August 2019; Accepted 2 October 2019 Available online 24 October 2019 0278-5846/ © 2019 Published by Elsevier Inc.
Progress in Neuropsychopharmacology & Biological Psychiatry 97 (2020) 109771
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details of the study subjects and statistical analyses can be found in the previously published study (van Rheenen et al., 2016). The GWAS summary statistics are publicly available through the Project MinE data browser at http://databrowser.projectmine.com/.
agent on clinical outcomes of a disease (Lawlor et al., 2008). With the given dataset of GDMs, we were able to estimate the causality relationship between genetically determined metabolites and the risk of development of ALS. The aim of the present study was to (1) estimate the causal relationships between non-targeted human serum metabolites and the development of ALS; (2) appraise the evidence for direction and robustness in the estimated etiological associations; (3) investigate potential genetic variants that lead to the variation of ALSrelated metabolites, which might also contribute to the progress of ALS.
2.4. Statistical analysis for Mendelian randomization We used the standard inverse variance weighted (IVW) method as the primary MR analysis for estimating the causal relationships between serum metabolites and ALS (Burgess et al., 2013). The IVW approach provided a consistent estimate of the causal effects of the exposure on the outcome, with the assumption that all variants were valid instruments and unaffected by pleiotropy (Brion et al., 2013). Though IVW presents an efficient method and is widely applied as the preferred method for MR analysis, bias might exist when some genetic variants are invalid. To control for this, we applied three additional MR methods as sensitivity analysis: the weighted median method, which allowed a subset (< 50%) of the genetic variants to be invalid instrumental variables or present pleiotropy (Bowden et al., 2016); MR-Egger, which worked even when > 50% of the variants were invalid (Bowden et al., 2015); MR-PRESSO, which is a recently developed method that can detect and correct for horizontal pleiotropic outliers in multi-instrument summary-level MR testing (Verbanck et al., 2018). Furthermore, we conducted a MR-PRESSO Global test to detect whether horizontal pleiotropy existed. All statistical analyses were carried out using the R software (https://www.r-project.org/), and the results were considered statistically significant when the P value < .05.
2. Methods 2.1. Genetically determined levels of serum metabolites We obtained the summary-level statistics from genome-wide association studies for 486 serum metabolites from Shin's study (Shin et al., 2014). A total of 7824 adult individuals from 2 European cohorts were included in the GWAS analysis. Metabolomics data were acquired based on non-targeted mass spectrometry analysis of human fasting serum. Metabolites were identified by automated comparison of the ion features with a reference library of chemical standard entries, followed by a quality control procedure using software developed at Metabolon, Inc. (https://www.metabolon.com/). Finally, a subset of 486 metabolites was available for GWAS analysis in both the cohorts, including 309 known and 177 unknown metabolites. The 309 known metabolites were further classified into 8 broad metabolic groups (amino acids, carbohydrates, cofactors and vitamins, energy, lipids, nucleotides, peptides and xenobiotic metabolism), as described in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database (Kanehisa et al., 2012). Genotyping and imputation were carried out separately for the 2 cohorts, and approximately 2.1 million single nucleotide polymorphisms (SNPs) present in both the cohorts were tested in the primary genomewide association analysis. Full GWAS summary statistics are publicly available through the Metabolomics GWAS server at http:// metabolomics.helmholtz-muenchen.de/gwas/.
3. Results 3.1. Instrumental variables for Mendelian randomization Following a uniform procedure, we obtained 3–675 independent SNPs to construct instrumental variables for the 486 metabolites. On an average, these generated instrumental variables could explain 4.7% (range 0.8–83.5%) of the variance in their respective metabolites. The average F statistic, another measurement to test the strength of these instrumental variables, was 22.33 (range 20.33–66.06). All instrumental variables for the 486 metabolites were sufficiently informative (F statistic > 10) for MR analyses.
2.2. Selection of metabolite-associated genetic instruments For each of the 486 metabolites, we selected SNPs that showed association at P < 1 × 10−5 to construct the instrumental variable of the specific metabolite. This threshold was suggested to avoid weak instrumental bias and explain a larger variance of the related metabolite (Burgess et al., 2013). We next performed a clumping procedure with European 1000G as a reference panel to pick out the independent variants, setting a linkage disequilibrium threshold of r2 < 0.1 in a 500-kb window. The variance was explained (adjusted r2), and the F statistic for each metabolite was further calculated to assess the strength of these instrumental variables (Pierce et al., 2011). Typically, an F statistic > 10 is considered as a typical threshold for selection of strong instrumental variables (Brion et al., 2013).
3.2. Effects of genetically determined metabolites on ALS The IVW identified 18 metabolites that showed causal relationship with ALS (Fig. 1), including 6 lipids, 4 amino acids, 3 peptides, 2 xenobiotics, an energy metabolite, and 2 unknown metabolites. We found that 1-arachidonoylglycerophosphoinositol was the most significant lipid for ALS pathogenesis. Using 22 SNPs as instrumental predictors, we observed a 1-fold increased risk of developing ALS per 1-Standard deviation (SD) increase in the level of 1-arachidonoylglycerophosphoinositol (Odds ratio (OR) = 2.10; 95% Confidence interval (CI): 1.30–3.39). Other lipids, like myristate (OR = 1.75; 95% CI: 1.05–2.92), lathosterol (OR = 1.43; 95% CI: 1.05–1.93), and deoxycholate (OR = 1.27; 95% CI: 1.01–1.61) were also found to be associated with high risk of developing ALS, while 1-stearoylglycerophosphoethanolamine (OR = 0.54; 95% CI: 0.35–0.83), and pentadecanoate (OR = 0.41; 95% CI: 0.22–0.78) showed inverse relationships with ALS. In case of amino acids, 3-methyl-2-oxobutyrate (OR = 3.15; 95% CI: 1.36–7.28), 5-oxoproline (OR = 2.00; 95% CI: 1.01–3.97), and, 2-methylbutyroylcarnitine (OR = 1.96; 95% CI: 1.06–3.64) were associated with high risk of developing ALS, while 4acetamidobutanoate (OR = 0.48; 95% CI: 0.24–0.94) was associated with low risk of developing ALS. In case of peptides, the OR (95% CI) was 1.98 (1.04,3.75) for gamma-glutamylleucine, 1.94 (1.01,3.74) for gamma-glutamylphenylalanine, and 0.50 (0.26,0.99) for gamma-glutamylthreonine. The only identified energy generating molecule,
2.3. GWAS summary dataset of ALS GWAS summary statistics of ALS was obtained from a recent GWAS with 12,577 cases and 23,475 controls from 41 cohorts(van Rheenen et al., 2016). Patients were diagnosed with possible, probable, or definite ALS according to either the 1994 El-Escorial Criteria or the revised the El Escorial criteria (Brooks, 1994; Brooks et al., 2000). Whole genomic SNP genotyping was conducted separately, and 27 case-control strata were formed according to the genotyping platforms and nationalities. After quality control, 8,697,640 variants were included in the final association analysis. Association tests were conducted by SNPTEST (https://mathgen.stats.ox.ac.uk/genetics_software/snptest/ snptest.html) separately for each stratum, and the results were then combined with an inverse-variance-weighted, fixed-effect meta-analyses using METAL (https://genome.sph.umich.edu/wiki/METAL). The 2
Progress in Neuropsychopharmacology & Biological Psychiatry 97 (2020) 109771
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Metabolites Lipid 1-arachidonoylglycerophosphoinositol myristate (14:0) lathosterol deoxycholate 1-stearoylglycerophosphoethanolamine pentadecanoate (15:0)
P Value
SNPs
OR (95% CI)
Lower Risk Higher Risk
22 25 18 17 14 20
2.10(1.30,3.39) 1.75(1.05,2.92) 1.43(1.05,1.93) 1.27(1.01,1.61) 0.54(0.35,0.83) 0.41(0.22,0.78)
0.0024 0.0332 0.0214 0.0486 0.0049 0.0062
18 26 27 39
3.15(1.36,7.28) 2.00(1.01,3.97) 1.96(1.06,3.64) 0.48(0.24,0.94)
0.0074 0.0483 0.0327 0.0316
38 38 10
1.98(1.04,3.75) 1.94(1.01,3.74) 0.50(0.26,0.99)
0.0372 0.0478 0.0466
475 13
0.98(0.96,0.99) 0.90(0.84,0.98)
0.0009 0.0117
49
0.53(0.30,0.93)
0.0277
13 48
1.72(1.16,2.56) 0.48(0.23,0.97)
0.0076 0.0403
Amino acid 3-methyl-2-oxobutyrate 5-oxoproline 2-methylbutyroylcarnitine 4-acetamidobutanoate
Peptide gamma-glutamylleucine gamma-glutamylphenylalanine gamma-glutamylthreonine
Xenobiotics 2-methoxyacetaminophen sulfate 2-hydroxyhippurate (salicylurate)
Energy citrate
Unknown X-12627 X-12100
0
2
4
6
8
Effects of 1-SD Increase in Genetically Determined Metabolites on ALS
Fig. 1. Mendelian randomization results for the serum metabolites and the risks they pose in development of ALS.
instrumental variables in the MR analysis. Table 2 shows the leading SNPs that showed large association coefficients for both the metabolites and ALS. We focused on the leading SNPs because they were expected to have strong effects on both, the metabolites and ALS. All the leading SNPs were derived at the significance threshold of P < 1 × 10−5 for metabolites, and some SNPs also showed significant signals for ALS, such as rs7121134 (P = .0027), rs9642829 (P = .0071), rs16948533 (P = .0200) and, rs1985243 (P = .0198). These genetic variants played decisive roles in determining the association between metabolites and ALS.
citrate, was associated with 50% decreased risk of developing ALS per 1-SD increase. Besides, we also detected two xenobiotics, which usually originate from drugs. However, the effects were low for every 1-SD variation in both the metabolites (OR = 0.98; 95% CI: 0.96–0.99 for 2methoxyacetaminophen sulfate, and OR = 0.90; 95% CI: 0.84–0.98 for 2-hydroxyhippurate). 3.3. Sensitivity analysis results The results for the sensitivity analyses of the 18 IVW-identified metabolites are shown in Table 1. Gamma-glutamylphenylalanine was the only metabolite that presented robust associations across all additional MR methods (PMR-Egger = 0.0381; PWeighted-median = 0.0144, and PMR-PRESSO = 0.0365), and there was no evidence of horizontal pleiotropy (PGlobal test = 0.7284). We found that gamma-glutamylphenylalanine was associated with a high OR (1.94–4.64) for development of ALS by using 38 SNPs as proxies (variance explained = 10.0%; F statistic = 23.32) (Fig. 2). The other two peptides, gamma-glutamylleucine (PWeighted-median = 0.0020; PMR-PRESSO = 0.0442), and gamma-glutamylthreonine (PWeighted-median = 0.0280) also showed robust effects on ALS, though not all MR methods yielded significant results. In addition, there were several other metabolites that showed suggestive associations with ALS when sensitivity analyses were conducted, such as lathosterol (PWeighted-median = 0.0473; PMR-PRESSO = 0.0343), 1stearoylglycerophosphoethanolamine (PWeighted-median = 0.0240; PMRPRESSO = 0.0016), 2-methylbutyroylcarnitine (PMR-Egger = 0.0008; PMR4-acetamidobutanoate (PWeighted-median = 0.0260; PRESSO = 0.0423), PMR-PRESSO = 0.0382), and 2-methoxyacetaminophen sulfate (PMREgger = 0.0209; PMR-PRESSO = 0.0008).
4. Discussion We performed an MR analysis to provide an unbiased estimate of the causal effects of human serum metabolites on ALS. Using genetic variants as instrumental variables, we observed a 1-fold higher risk of developing ALS per 1-SD increase in the levels of gamma-glutamylphenylalanine. Our study also suggested that the SNP rs1985243 might play an important role in explaining the relationship between gammaglutamylphenylalanine and ALS. To the best of our knowledge, this is the first study that uses genetic variants as proxies to assess the causality relationships between serum metabolites and ALS. Our study detected 18 metabolites that might have causal effects on the development of ALS. Among these metabolites, a group of gammaglutamyl amino acids was identified: gamma-glutamylphenylalanine, gamma-glutamylleucine, and gamma-glutamylthreonine. The gammaglutamyl amino acids are dipeptides that are common intermediate metabolites of the gamma-glutamyl cycle (Orlowski and Wilk, 1975). The gamma-glutamyl cycle is responsible for glutathione (GSH) metabolism in human bodies, where GSH is degraded by the sequential reaction of gamma-glutamyl transpeptidase (GGT), gamma-glutamyl cyclotransferase, and 5-oxoprolinase to yield glutamate (Glu) and dipeptides (Hlozek et al., 2017; Ohkama-Ohtsu et al., 2008). Interestingly, our study also found that 5-oxoproline is associated with high
3.4. Potential genetic variants (SNPs) responsible for both metabotypes and ALS We further reported the genetic variants that were used as 3
Progress in Neuropsychopharmacology & Biological Psychiatry 97 (2020) 109771
L. Yang, et al.
Table 1 Sensitivity analysis results for the 18 metabolites identified by the IVW method. Metabolites
MR-Egger
Lipid 1-arachidonoylglycerophosphoinositol Myristate (14:0) Lathosterol Deoxycholate 1-stearoylglycerophosphoethanolamine Pentadecanoate (15:0) Amino acid 3-methyl-2-oxobutyrate 5-oxoproline 2-methylbutyroylcarnitine 4-acetamidobutanoate Peptide Gamma-glutamylleucine Gamma-glutamylphenylalanine Gamma-glutamylthreonine Xenobiotics 2-methoxyacetaminophen sulfate 2-hydroxyhippurate (salicylurate) Energy Citrate Unknown X-12627 X-12100
Weighted median
MR-PRESSO
OR (95% CI)
P-value
OR (95% CI)
P-value
OR (95% CI)
P-value
RSS
P-value
2.28(0.90,5.73) 1.17(0.44,3.12) 1.46(0.80,2.67) 1.23(0.72,2.11) 0.80(0.30,2.13) 0.40(0.07,2.26)
0.0813 0.7590 0.2140 0.4544 0.6509 0.2991
1.74(0.83,3.62) 1.21(0.55,2.65) 1.49(1.01,2.22) 1.21(0.87,1.68) 0.51(0.28,0.91) 0.45(0.18,1.12)
0.1410 0.6356 0.0473 0.2529 0.0240 0.0848
2.10(1.38,3.20) 1.75(1.05,2.91) 1.43(1.05,1.93) 1.27(1.04,1.56) 0.54(0.39,0.73) 0.41(0.23,0.71)
0.0024 0.0428 0.0343 0.0357 0.0016 0.0051
17.94 25.53 22.19 13.17 7.23 15.8
0.7612 0.5043 0.3025 0.7723 0.9409 0.7825
12.66(0.62,256.65) 2.21(0.68,7.16) 19.68(3.47,111.71) 1.59(0.16,15.68)
0.0982 0.1876 0.0008 0.6899
2.64(0.83,8.47) 2.15(0.87,5.33) 1.70(0.75,3.83) 0.33(0.12,0.88)
0.1018 0.0976 0.2043 0.0260
3.15(1.53,6.46) 2.00(1.01,3.97) 1.96(1.06,3.64) 0.48(0.24,0.94)
0.0062 0.0599 0.0423 0.0382
14.26 27.96 32.63 42.21
0.7603 0.3810 0.2697 0.3461
1.33(0.31,5.61) 4.64(1.09,19.81) 2.96(0.51,17.11)
0.7010 0.0381 0.2245
4.18(1.68,10.37) 3.43(1.28,9.20) 0.36(0.15,0.90)
0.0020 0.0144 0.0280
1.98(1.04,3.75) 1.94(1.07,3.53) 0.50(0.26,0.99)
0.0442 0.0365 0.0778
44.8 32.34 13.2
0.2499 0.7284 0.3091
0.96(0.92,0.99) 0.94(0.81,1.08)
0.0209 0.3726
0.98(0.96,0.99) 0.91(0.81,1.03)
0.0561 0.133
0.98(0.96,0.99) 0.90(0.84,0.97)
0.0008 0.0139
429.59 10.27
0.6069 0.7433
0.34(0.06,2.07)
0.2411
0.56(0.25,1.26)
0.1582
0.53(0.32,0.89)
0.0199
41.87
0.7818
1.26(0.36,4.37) 0.03(0.01,0.80)
0.7148 0.0363
1.79(1.02,3.17) 0.25(0.10,0.61)
0.0443 0.0023
1.72(1.16,2.54) 0.48(0.23,0.97)
0.0185 0.0460
13.81 70.88
0.4848 0.0221
risk of developing ALS. These findings suggest that GSH metabolism dysfunction might account for the pathogenesis of ALS. Notably, GSH is an important antioxidant, while oxidative stress is a known risk factor for ALS (Baillet et al., 2010; Njalsson, 2005). A recent study also found elevated concentration of glutathione disulfide (GSSG) and lower concentration of GSH in ALS patients (Blasco et al., 2017). Thus, the oxidative stress caused by GSH metabolism dysfunction might be an important risk factor associated with pathogenesis of ALS. Besides the GSH metabolism dysfunction, other processes might also lead to ALS, such as
glutamate-induced excitotoxicity (Heath and Shaw, 2002; Meldrum and Garthwaite, 1990). These hypotheses need to be confirmed by further studies. 1-stearoylglycerophosphoethanolamine is a lysophospholipid (LP) that showed causal relationship with ALS, in this study. Lysophospholipids (LPs), especially sphingosine 1-phosphate (S1P), are bioactive lipids that transduce signals through their specific cell-surface G protein-coupled receptors. S1P metabolism has long been associated with neurodegenerative diseases, such as Huntington's disease,
0.1
Genetic effect on ALS
Globle test
0.0
-0.1 IVW OR (95% CI) : 1.94(1.01,3.74) Weighted Median OR (95% CI) : 3.43(1.28,9.20) MR Egger OR (95% CI) : 4.64(1.09,19.81)
-0.2 0.00
0.02
0.04
0.06
Genetic effet on gamma-glutamylphenylalanine Fig. 2. Genetic effects of SNPs on gamma-glutamylphenylalanine and ALS. 4
Progress in Neuropsychopharmacology & Biological Psychiatry 97 (2020) 109771
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Table 2 Leading SNPs for the identified metabolites and their associations with ALS. Metabolites
Leading SNP
Gene
For metabotype
For ALS
β
SE
P Value
β
SE
P Value
Lipid 1-arachidonoylglycerophosphoinositol Myristate (14:0) Lathosterol Deoxycholate 1-stearoylglycerophosphoethanolamine Pentadecanoate (15:0)
rs1764109 rs13393013 rs2466053 rs12405017 rs12981207 rs3900774
/ LOC105376755 NRG1 DDAH1 AKAP8 SMOC2
0.0082 0.0415 0.1851 −0.1891 0.0496 0.0610
0.0018 0.0085 0.0386 0.0426 0.0110 0.0130
7.20E-06 1.08E-06 1.63E-06 9.18E-06 6.16E-06 2.70E-06
0.0159 0.0761 0.0655 −0.1031 −0.0238 −0.1168
0.0212 0.0546 0.0661 0.0711 0.0337 0.0579
0.4531 0.1635 0.3222 0.1469 0.4794 0.0434
Amino acid 3-methyl-2-oxobutyrate 5-oxoproline 2-methylbutyroylcarnitine 4-acetamidobutanoate
rs16948533 rs11986602 rs9642829 rs1538330
LOC100506974 EXOSC4 / /
−0.0224 −0.0620 −0.0346 −0.0306
0.0047 0.0029 0.0075 0.0064
2.23E-06 1.07E-104 3.61E-06 1.73E-06
−0.0649 −0.0474 −0.1595 0.0509
0.0279 0.0307 0.0593 0.0586
0.0200 0.1227 0.0071 0.3847
Peptide Gamma-glutamylleucine Gamma-glutamylphenylalanine Gamma-glutamylthreonine
rs7835053 rs1985243 rs12216313
/ BCAR3 /
−0.0398 −0.0524 −0.0429
0.0086 0.0117 0.0094
3.37E-06 7.59E-06 5.49E-06
−0.0777 −0.0878 0.0232
0.0567 0.0377 0.0525
0.1706 0.0198 0.6582
Xenobiotics 2-methoxyacetaminophen sulfate 2-hydroxyhippurate (salicylurate)
rs7121134 rs919028
INSC RAP1GDS1
−0.3388 −0.5485
0.0741 0.1233
4.90E-06 8.69E-06
0.2539 0.0513
0.0846 0.0386
0.0027 0.1840
Energy Citrate
rs6940937
GRM1
0.0384
0.0086
7.07E-06
−0.0972
0.0716
0.1745
Unknown X-12627 X-12100
rs7148806 rs7922903
/ SLIT1
−0.0474 −0.0168
0.0107 0.0035
9.78E-06 2.10E-06
−0.0489 0.0723
0.0219 0.0294
0.0257 0.0137
5. Conclusions
Alzheimer's disease, and multiple sclerosis (Moruno-Manchon et al., 2017; Ramesh et al., 2018; van Echten-Deckert et al., 2014). Fingolimod, an S1P receptor modulator, was the first FDA-approved medicine as an orally bioavailable drug for treating relapsing forms of multiple sclerosis (Kihara et al., 2015). Evidence also shows that fingolimod could induce neuroprotective factors in human astrocytes (Hoffmann et al., 2015). Thus, 1-stearoylglycerophosphoethanolamine might provide valuable information for drug discovery for ALS. 2-methoxyacetaminophen sulfate, a xenobiotic that showed causal relationship with ALS, is a member of the class of acetamides. 2-methoxyacetaminophen sulfate can irreversibly bind to GSH, lead to subsequent disruption of the gamma glutamyl cycle, and cause excessive 5oxoproline generation. Citrate is an intermediate metabolite in the tricarboxylic acid (TCA) cycle. Changes in citrate concentration might reflect abnormalities in mitochondrial function, which has also been considered as a causal factor in the pathogenesis of ALS (Chaturvedi and Flint Beal, 2013). We further focused on the potential genetic variants that could determine the variation of the metabolites and ultimately lead to the development of ALS. For example, rs1985243 was the leading SNP (β = −0.0524, SE = 0.0117, P = 7.59E-06) for determining the variation of gamma-glutamylphenylalanine, this SNP also showed causal effects on ALS (β = −0.0878, SE = 0.0377, P = .0198). Based on this fact, we were able to propose the hypothesis that rs1985243 might contribute to the development of ALS through affecting the levels of gamma-glutamylphenylalanine. Although much remains unknown, it can be said that the generated SNPs play important roles in revealing the pathogenesis or therapeutic targets for ALS. Our study also has certain limitations: 1) The metabolites identified for ALS need to be verified by further studies. 2) The database (GDM) used in this study is based on European populations; thus, further studies should be done to include samples with multi-ethnicity to provide a more accurate assessment of the genetic influences on metabolites. 3) We exhibited the genetic variants that might help to elucidate pathophysiological mechanism or therapeutic targets for ALS. However, the biological functions for these variants need to be further explored.
In this study we performed an MR analysis to identify ALS-related metabolites. Our study detected 18 metabolites that might have causal effects on the development of ALS, including a group of gamma-glutamyl amino acids. Our findings suggest that GSH metabolism dysfunction might be involved in the pathogenesis of ALS. A set of genetic variants, such as the SNP rs1985243, which could help inform pathophysiological mechanism or therapeutic targets for ALS, are also shown in our study. Our study provides a novel insight in combining metabolomics with genomics to reveal the pathogenesis and therapeutic strategies for ALS. Declaration of Competing Interest The authors declare no competing financial interests. Acknowledgements We would like to thank the High-Performance Computing Cluster of the First Affiliated Hospital of Xi'an Jiaotong University for data computing. The study was supported by the National Natural Science Foundation of China (81071952). References Baillet, A., Chanteperdrix, V., Trocme, C., Casez, P., Garrel, C., Besson, G., 2010. The role of oxidative stress in amyotrophic lateral sclerosis and Parkinson’s disease. Neurochem. Res. 35, 1530–1537. https://doi.org/10.1007/s11064-010-0212-5. Blasco, H., Garcon, G., Patin, F., Veyrat-Durebex, C., Boyer, J., Devos, D., Vourc’h, P., Andres, C.R., Corcia, P., 2017. Panel of oxidative stress and inflammatory biomarkers in ALS: a pilot study. Can. J. Neurol. Sci. 44, 90–95. https://doi.org/10.1017/cjn. 2016.284. Bowden, J., Davey Smith, G., Burgess, S., 2015. Mendelian randomization with invalid instruments: effect estimation and bias detection through egger regression. Int. J. Epidemiol. 44, 512–525. https://doi.org/10.1093/ije/dyv080. Bowden, J., Davey Smith, G., Haycock, P.C., Burgess, S., 2016. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314. https://doi.org/10.1002/gepi.21965.
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Causal effects of serum metabolites on amyotrophic lateral sclerosis: A Mendelian randomization study Lihong Yanga, Xiaohong Lvb, Hanzhi Duc, Di Wuc, Mengchang Wangc,
T
⁎
a
Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China Department of Rheumatism and Immunology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China c Department of Hematopathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Serum metabolite Amyotrophic lateral sclerosis Mendelian randomization GSH metabolism dysfunction
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder that is affected by both genetic and environmental factors. Nowadays, OMIC technologies, such as genomics and metabolomics, are providing a systematic readout of genetic structures and physiological states for understanding human diseases. However, the comprehensive analysis of cross-omics is often lacking. Here, we conducted a Mendelian randomization analysis to provide a comprehensive analysis of metabolomics and genomics to estimate the causal relationships between non-targeted human serum metabolites and the development of ALS. Using genetic variants as predictors, our study detected 18 metabolites that might have causal effects on the development of ALS, including a group of gamma-glutamyl amino acids. Our findings suggested that glutathione metabolism dysfunction might be involved in the pathogenesis of ALS. Furthermore, our study provides a novel method to understand the pathogenesis of human diseases and develop therapeutic strategies for diseases by combining metabolomics with genomics.
1. Introduction Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease that is characterized by degeneration of both upper and lower motor neurons (Hardiman et al., 2017; Kiernan et al., 2011). The clinical symptoms begin insidiously with focal weakness, but spread relentlessly to most muscles, including the diaphragm (Brown and AlChalabi, 2017). ALS generally leads to the death of the patient within 2–5 years of appearance of the first symptoms, due to complications in respiratory functions (Costa and de Carvalho, 2016; de Carvalho et al., 1996). So far, there have been no effective treatments for patients with ALS, despite a large number of clinical trials. Extensive efforts have been made to identify novel biomarkers that could help in early diagnosis, understanding disease progression, and finding therapeutic targets for ALS (Kruger et al., 2013; Otto et al., 2012; Tarasiuk et al., 2012; Turner et al., 2013a, b). However, the progress so far has been modest in terms of understanding the pathophysiological mechanism underlying the development of ALS. Genetic predispositions and their interactions with environmental factors play a crucial role in the development of ALS(Riva et al., 2016).
Currently, OMIC technologies, such as genomics and metabolomics, are widely applied to identify potential markers that could help unravel pathophysiological mechanisms or therapeutic strategies for human diseases (Holmes et al., 2008). Genome wide association studies (GWAS) and metabolomics have identified many potential genetic variants and metabolites, respectively, that might contribute to the development of ALS (Fogh et al., 2014; Laaksovirta et al., 2010; Mitropoulos et al., 2018; van Es et al., 2009). However, these findings are often scattered and provide little information about the underlying biological processes of ALS. A comprehensive analysis is needed to understand the relationships and interactions between genetic variations and metabolic factors, and their potential roles in causing ALS. Recently, GWAS were extended to metabolic phenotypes which generated an atlas of genetically determined metabolites (GDMs) (Gieger et al., 2008; Kettunen et al., 2012; Shin et al., 2014; Suhre et al., 2011). The GDMs constitute functional intermediates that reveal the role of the interactions between genetic variants and metabolites in the pathogenesis of human diseases. Mendelian randomization (MR) is a novel technique that uses genetic variants as instrumental variables to assess the causality of an
Abbreviations: ALS, Amyotrophic lateral sclerosis; GWAS, Genome wide association studies; GDM, Genetically determined metabolites; GSH, glutathione; MR, Mendelian randomization; SNP, Single nucleotide polymorphism; IVW, Inverse variance weighted ⁎ Corresponding author. E-mail address: [email protected] (M. Wang). https://doi.org/10.1016/j.pnpbp.2019.109771 Received 5 August 2019; Accepted 2 October 2019 Available online 24 October 2019 0278-5846/ © 2019 Published by Elsevier Inc.
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details of the study subjects and statistical analyses can be found in the previously published study (van Rheenen et al., 2016). The GWAS summary statistics are publicly available through the Project MinE data browser at http://databrowser.projectmine.com/.
agent on clinical outcomes of a disease (Lawlor et al., 2008). With the given dataset of GDMs, we were able to estimate the causality relationship between genetically determined metabolites and the risk of development of ALS. The aim of the present study was to (1) estimate the causal relationships between non-targeted human serum metabolites and the development of ALS; (2) appraise the evidence for direction and robustness in the estimated etiological associations; (3) investigate potential genetic variants that lead to the variation of ALSrelated metabolites, which might also contribute to the progress of ALS.
2.4. Statistical analysis for Mendelian randomization We used the standard inverse variance weighted (IVW) method as the primary MR analysis for estimating the causal relationships between serum metabolites and ALS (Burgess et al., 2013). The IVW approach provided a consistent estimate of the causal effects of the exposure on the outcome, with the assumption that all variants were valid instruments and unaffected by pleiotropy (Brion et al., 2013). Though IVW presents an efficient method and is widely applied as the preferred method for MR analysis, bias might exist when some genetic variants are invalid. To control for this, we applied three additional MR methods as sensitivity analysis: the weighted median method, which allowed a subset (< 50%) of the genetic variants to be invalid instrumental variables or present pleiotropy (Bowden et al., 2016); MR-Egger, which worked even when > 50% of the variants were invalid (Bowden et al., 2015); MR-PRESSO, which is a recently developed method that can detect and correct for horizontal pleiotropic outliers in multi-instrument summary-level MR testing (Verbanck et al., 2018). Furthermore, we conducted a MR-PRESSO Global test to detect whether horizontal pleiotropy existed. All statistical analyses were carried out using the R software (https://www.r-project.org/), and the results were considered statistically significant when the P value < .05.
2. Methods 2.1. Genetically determined levels of serum metabolites We obtained the summary-level statistics from genome-wide association studies for 486 serum metabolites from Shin's study (Shin et al., 2014). A total of 7824 adult individuals from 2 European cohorts were included in the GWAS analysis. Metabolomics data were acquired based on non-targeted mass spectrometry analysis of human fasting serum. Metabolites were identified by automated comparison of the ion features with a reference library of chemical standard entries, followed by a quality control procedure using software developed at Metabolon, Inc. (https://www.metabolon.com/). Finally, a subset of 486 metabolites was available for GWAS analysis in both the cohorts, including 309 known and 177 unknown metabolites. The 309 known metabolites were further classified into 8 broad metabolic groups (amino acids, carbohydrates, cofactors and vitamins, energy, lipids, nucleotides, peptides and xenobiotic metabolism), as described in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database (Kanehisa et al., 2012). Genotyping and imputation were carried out separately for the 2 cohorts, and approximately 2.1 million single nucleotide polymorphisms (SNPs) present in both the cohorts were tested in the primary genomewide association analysis. Full GWAS summary statistics are publicly available through the Metabolomics GWAS server at http:// metabolomics.helmholtz-muenchen.de/gwas/.
3. Results 3.1. Instrumental variables for Mendelian randomization Following a uniform procedure, we obtained 3–675 independent SNPs to construct instrumental variables for the 486 metabolites. On an average, these generated instrumental variables could explain 4.7% (range 0.8–83.5%) of the variance in their respective metabolites. The average F statistic, another measurement to test the strength of these instrumental variables, was 22.33 (range 20.33–66.06). All instrumental variables for the 486 metabolites were sufficiently informative (F statistic > 10) for MR analyses.
2.2. Selection of metabolite-associated genetic instruments For each of the 486 metabolites, we selected SNPs that showed association at P < 1 × 10−5 to construct the instrumental variable of the specific metabolite. This threshold was suggested to avoid weak instrumental bias and explain a larger variance of the related metabolite (Burgess et al., 2013). We next performed a clumping procedure with European 1000G as a reference panel to pick out the independent variants, setting a linkage disequilibrium threshold of r2 < 0.1 in a 500-kb window. The variance was explained (adjusted r2), and the F statistic for each metabolite was further calculated to assess the strength of these instrumental variables (Pierce et al., 2011). Typically, an F statistic > 10 is considered as a typical threshold for selection of strong instrumental variables (Brion et al., 2013).
3.2. Effects of genetically determined metabolites on ALS The IVW identified 18 metabolites that showed causal relationship with ALS (Fig. 1), including 6 lipids, 4 amino acids, 3 peptides, 2 xenobiotics, an energy metabolite, and 2 unknown metabolites. We found that 1-arachidonoylglycerophosphoinositol was the most significant lipid for ALS pathogenesis. Using 22 SNPs as instrumental predictors, we observed a 1-fold increased risk of developing ALS per 1-Standard deviation (SD) increase in the level of 1-arachidonoylglycerophosphoinositol (Odds ratio (OR) = 2.10; 95% Confidence interval (CI): 1.30–3.39). Other lipids, like myristate (OR = 1.75; 95% CI: 1.05–2.92), lathosterol (OR = 1.43; 95% CI: 1.05–1.93), and deoxycholate (OR = 1.27; 95% CI: 1.01–1.61) were also found to be associated with high risk of developing ALS, while 1-stearoylglycerophosphoethanolamine (OR = 0.54; 95% CI: 0.35–0.83), and pentadecanoate (OR = 0.41; 95% CI: 0.22–0.78) showed inverse relationships with ALS. In case of amino acids, 3-methyl-2-oxobutyrate (OR = 3.15; 95% CI: 1.36–7.28), 5-oxoproline (OR = 2.00; 95% CI: 1.01–3.97), and, 2-methylbutyroylcarnitine (OR = 1.96; 95% CI: 1.06–3.64) were associated with high risk of developing ALS, while 4acetamidobutanoate (OR = 0.48; 95% CI: 0.24–0.94) was associated with low risk of developing ALS. In case of peptides, the OR (95% CI) was 1.98 (1.04,3.75) for gamma-glutamylleucine, 1.94 (1.01,3.74) for gamma-glutamylphenylalanine, and 0.50 (0.26,0.99) for gamma-glutamylthreonine. The only identified energy generating molecule,
2.3. GWAS summary dataset of ALS GWAS summary statistics of ALS was obtained from a recent GWAS with 12,577 cases and 23,475 controls from 41 cohorts(van Rheenen et al., 2016). Patients were diagnosed with possible, probable, or definite ALS according to either the 1994 El-Escorial Criteria or the revised the El Escorial criteria (Brooks, 1994; Brooks et al., 2000). Whole genomic SNP genotyping was conducted separately, and 27 case-control strata were formed according to the genotyping platforms and nationalities. After quality control, 8,697,640 variants were included in the final association analysis. Association tests were conducted by SNPTEST (https://mathgen.stats.ox.ac.uk/genetics_software/snptest/ snptest.html) separately for each stratum, and the results were then combined with an inverse-variance-weighted, fixed-effect meta-analyses using METAL (https://genome.sph.umich.edu/wiki/METAL). The 2
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Metabolites Lipid 1-arachidonoylglycerophosphoinositol myristate (14:0) lathosterol deoxycholate 1-stearoylglycerophosphoethanolamine pentadecanoate (15:0)
P Value
SNPs
OR (95% CI)
Lower Risk Higher Risk
22 25 18 17 14 20
2.10(1.30,3.39) 1.75(1.05,2.92) 1.43(1.05,1.93) 1.27(1.01,1.61) 0.54(0.35,0.83) 0.41(0.22,0.78)
0.0024 0.0332 0.0214 0.0486 0.0049 0.0062
18 26 27 39
3.15(1.36,7.28) 2.00(1.01,3.97) 1.96(1.06,3.64) 0.48(0.24,0.94)
0.0074 0.0483 0.0327 0.0316
38 38 10
1.98(1.04,3.75) 1.94(1.01,3.74) 0.50(0.26,0.99)
0.0372 0.0478 0.0466
475 13
0.98(0.96,0.99) 0.90(0.84,0.98)
0.0009 0.0117
49
0.53(0.30,0.93)
0.0277
13 48
1.72(1.16,2.56) 0.48(0.23,0.97)
0.0076 0.0403
Amino acid 3-methyl-2-oxobutyrate 5-oxoproline 2-methylbutyroylcarnitine 4-acetamidobutanoate
Peptide gamma-glutamylleucine gamma-glutamylphenylalanine gamma-glutamylthreonine
Xenobiotics 2-methoxyacetaminophen sulfate 2-hydroxyhippurate (salicylurate)
Energy citrate
Unknown X-12627 X-12100
0
2
4
6
8
Effects of 1-SD Increase in Genetically Determined Metabolites on ALS
Fig. 1. Mendelian randomization results for the serum metabolites and the risks they pose in development of ALS.
instrumental variables in the MR analysis. Table 2 shows the leading SNPs that showed large association coefficients for both the metabolites and ALS. We focused on the leading SNPs because they were expected to have strong effects on both, the metabolites and ALS. All the leading SNPs were derived at the significance threshold of P < 1 × 10−5 for metabolites, and some SNPs also showed significant signals for ALS, such as rs7121134 (P = .0027), rs9642829 (P = .0071), rs16948533 (P = .0200) and, rs1985243 (P = .0198). These genetic variants played decisive roles in determining the association between metabolites and ALS.
citrate, was associated with 50% decreased risk of developing ALS per 1-SD increase. Besides, we also detected two xenobiotics, which usually originate from drugs. However, the effects were low for every 1-SD variation in both the metabolites (OR = 0.98; 95% CI: 0.96–0.99 for 2methoxyacetaminophen sulfate, and OR = 0.90; 95% CI: 0.84–0.98 for 2-hydroxyhippurate). 3.3. Sensitivity analysis results The results for the sensitivity analyses of the 18 IVW-identified metabolites are shown in Table 1. Gamma-glutamylphenylalanine was the only metabolite that presented robust associations across all additional MR methods (PMR-Egger = 0.0381; PWeighted-median = 0.0144, and PMR-PRESSO = 0.0365), and there was no evidence of horizontal pleiotropy (PGlobal test = 0.7284). We found that gamma-glutamylphenylalanine was associated with a high OR (1.94–4.64) for development of ALS by using 38 SNPs as proxies (variance explained = 10.0%; F statistic = 23.32) (Fig. 2). The other two peptides, gamma-glutamylleucine (PWeighted-median = 0.0020; PMR-PRESSO = 0.0442), and gamma-glutamylthreonine (PWeighted-median = 0.0280) also showed robust effects on ALS, though not all MR methods yielded significant results. In addition, there were several other metabolites that showed suggestive associations with ALS when sensitivity analyses were conducted, such as lathosterol (PWeighted-median = 0.0473; PMR-PRESSO = 0.0343), 1stearoylglycerophosphoethanolamine (PWeighted-median = 0.0240; PMRPRESSO = 0.0016), 2-methylbutyroylcarnitine (PMR-Egger = 0.0008; PMR4-acetamidobutanoate (PWeighted-median = 0.0260; PRESSO = 0.0423), PMR-PRESSO = 0.0382), and 2-methoxyacetaminophen sulfate (PMREgger = 0.0209; PMR-PRESSO = 0.0008).
4. Discussion We performed an MR analysis to provide an unbiased estimate of the causal effects of human serum metabolites on ALS. Using genetic variants as instrumental variables, we observed a 1-fold higher risk of developing ALS per 1-SD increase in the levels of gamma-glutamylphenylalanine. Our study also suggested that the SNP rs1985243 might play an important role in explaining the relationship between gammaglutamylphenylalanine and ALS. To the best of our knowledge, this is the first study that uses genetic variants as proxies to assess the causality relationships between serum metabolites and ALS. Our study detected 18 metabolites that might have causal effects on the development of ALS. Among these metabolites, a group of gammaglutamyl amino acids was identified: gamma-glutamylphenylalanine, gamma-glutamylleucine, and gamma-glutamylthreonine. The gammaglutamyl amino acids are dipeptides that are common intermediate metabolites of the gamma-glutamyl cycle (Orlowski and Wilk, 1975). The gamma-glutamyl cycle is responsible for glutathione (GSH) metabolism in human bodies, where GSH is degraded by the sequential reaction of gamma-glutamyl transpeptidase (GGT), gamma-glutamyl cyclotransferase, and 5-oxoprolinase to yield glutamate (Glu) and dipeptides (Hlozek et al., 2017; Ohkama-Ohtsu et al., 2008). Interestingly, our study also found that 5-oxoproline is associated with high
3.4. Potential genetic variants (SNPs) responsible for both metabotypes and ALS We further reported the genetic variants that were used as 3
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Table 1 Sensitivity analysis results for the 18 metabolites identified by the IVW method. Metabolites
MR-Egger
Lipid 1-arachidonoylglycerophosphoinositol Myristate (14:0) Lathosterol Deoxycholate 1-stearoylglycerophosphoethanolamine Pentadecanoate (15:0) Amino acid 3-methyl-2-oxobutyrate 5-oxoproline 2-methylbutyroylcarnitine 4-acetamidobutanoate Peptide Gamma-glutamylleucine Gamma-glutamylphenylalanine Gamma-glutamylthreonine Xenobiotics 2-methoxyacetaminophen sulfate 2-hydroxyhippurate (salicylurate) Energy Citrate Unknown X-12627 X-12100
Weighted median
MR-PRESSO
OR (95% CI)
P-value
OR (95% CI)
P-value
OR (95% CI)
P-value
RSS
P-value
2.28(0.90,5.73) 1.17(0.44,3.12) 1.46(0.80,2.67) 1.23(0.72,2.11) 0.80(0.30,2.13) 0.40(0.07,2.26)
0.0813 0.7590 0.2140 0.4544 0.6509 0.2991
1.74(0.83,3.62) 1.21(0.55,2.65) 1.49(1.01,2.22) 1.21(0.87,1.68) 0.51(0.28,0.91) 0.45(0.18,1.12)
0.1410 0.6356 0.0473 0.2529 0.0240 0.0848
2.10(1.38,3.20) 1.75(1.05,2.91) 1.43(1.05,1.93) 1.27(1.04,1.56) 0.54(0.39,0.73) 0.41(0.23,0.71)
0.0024 0.0428 0.0343 0.0357 0.0016 0.0051
17.94 25.53 22.19 13.17 7.23 15.8
0.7612 0.5043 0.3025 0.7723 0.9409 0.7825
12.66(0.62,256.65) 2.21(0.68,7.16) 19.68(3.47,111.71) 1.59(0.16,15.68)
0.0982 0.1876 0.0008 0.6899
2.64(0.83,8.47) 2.15(0.87,5.33) 1.70(0.75,3.83) 0.33(0.12,0.88)
0.1018 0.0976 0.2043 0.0260
3.15(1.53,6.46) 2.00(1.01,3.97) 1.96(1.06,3.64) 0.48(0.24,0.94)
0.0062 0.0599 0.0423 0.0382
14.26 27.96 32.63 42.21
0.7603 0.3810 0.2697 0.3461
1.33(0.31,5.61) 4.64(1.09,19.81) 2.96(0.51,17.11)
0.7010 0.0381 0.2245
4.18(1.68,10.37) 3.43(1.28,9.20) 0.36(0.15,0.90)
0.0020 0.0144 0.0280
1.98(1.04,3.75) 1.94(1.07,3.53) 0.50(0.26,0.99)
0.0442 0.0365 0.0778
44.8 32.34 13.2
0.2499 0.7284 0.3091
0.96(0.92,0.99) 0.94(0.81,1.08)
0.0209 0.3726
0.98(0.96,0.99) 0.91(0.81,1.03)
0.0561 0.133
0.98(0.96,0.99) 0.90(0.84,0.97)
0.0008 0.0139
429.59 10.27
0.6069 0.7433
0.34(0.06,2.07)
0.2411
0.56(0.25,1.26)
0.1582
0.53(0.32,0.89)
0.0199
41.87
0.7818
1.26(0.36,4.37) 0.03(0.01,0.80)
0.7148 0.0363
1.79(1.02,3.17) 0.25(0.10,0.61)
0.0443 0.0023
1.72(1.16,2.54) 0.48(0.23,0.97)
0.0185 0.0460
13.81 70.88
0.4848 0.0221
risk of developing ALS. These findings suggest that GSH metabolism dysfunction might account for the pathogenesis of ALS. Notably, GSH is an important antioxidant, while oxidative stress is a known risk factor for ALS (Baillet et al., 2010; Njalsson, 2005). A recent study also found elevated concentration of glutathione disulfide (GSSG) and lower concentration of GSH in ALS patients (Blasco et al., 2017). Thus, the oxidative stress caused by GSH metabolism dysfunction might be an important risk factor associated with pathogenesis of ALS. Besides the GSH metabolism dysfunction, other processes might also lead to ALS, such as
glutamate-induced excitotoxicity (Heath and Shaw, 2002; Meldrum and Garthwaite, 1990). These hypotheses need to be confirmed by further studies. 1-stearoylglycerophosphoethanolamine is a lysophospholipid (LP) that showed causal relationship with ALS, in this study. Lysophospholipids (LPs), especially sphingosine 1-phosphate (S1P), are bioactive lipids that transduce signals through their specific cell-surface G protein-coupled receptors. S1P metabolism has long been associated with neurodegenerative diseases, such as Huntington's disease,
0.1
Genetic effect on ALS
Globle test
0.0
-0.1 IVW OR (95% CI) : 1.94(1.01,3.74) Weighted Median OR (95% CI) : 3.43(1.28,9.20) MR Egger OR (95% CI) : 4.64(1.09,19.81)
-0.2 0.00
0.02
0.04
0.06
Genetic effet on gamma-glutamylphenylalanine Fig. 2. Genetic effects of SNPs on gamma-glutamylphenylalanine and ALS. 4
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Table 2 Leading SNPs for the identified metabolites and their associations with ALS. Metabolites
Leading SNP
Gene
For metabotype
For ALS
β
SE
P Value
β
SE
P Value
Lipid 1-arachidonoylglycerophosphoinositol Myristate (14:0) Lathosterol Deoxycholate 1-stearoylglycerophosphoethanolamine Pentadecanoate (15:0)
rs1764109 rs13393013 rs2466053 rs12405017 rs12981207 rs3900774
/ LOC105376755 NRG1 DDAH1 AKAP8 SMOC2
0.0082 0.0415 0.1851 −0.1891 0.0496 0.0610
0.0018 0.0085 0.0386 0.0426 0.0110 0.0130
7.20E-06 1.08E-06 1.63E-06 9.18E-06 6.16E-06 2.70E-06
0.0159 0.0761 0.0655 −0.1031 −0.0238 −0.1168
0.0212 0.0546 0.0661 0.0711 0.0337 0.0579
0.4531 0.1635 0.3222 0.1469 0.4794 0.0434
Amino acid 3-methyl-2-oxobutyrate 5-oxoproline 2-methylbutyroylcarnitine 4-acetamidobutanoate
rs16948533 rs11986602 rs9642829 rs1538330
LOC100506974 EXOSC4 / /
−0.0224 −0.0620 −0.0346 −0.0306
0.0047 0.0029 0.0075 0.0064
2.23E-06 1.07E-104 3.61E-06 1.73E-06
−0.0649 −0.0474 −0.1595 0.0509
0.0279 0.0307 0.0593 0.0586
0.0200 0.1227 0.0071 0.3847
Peptide Gamma-glutamylleucine Gamma-glutamylphenylalanine Gamma-glutamylthreonine
rs7835053 rs1985243 rs12216313
/ BCAR3 /
−0.0398 −0.0524 −0.0429
0.0086 0.0117 0.0094
3.37E-06 7.59E-06 5.49E-06
−0.0777 −0.0878 0.0232
0.0567 0.0377 0.0525
0.1706 0.0198 0.6582
Xenobiotics 2-methoxyacetaminophen sulfate 2-hydroxyhippurate (salicylurate)
rs7121134 rs919028
INSC RAP1GDS1
−0.3388 −0.5485
0.0741 0.1233
4.90E-06 8.69E-06
0.2539 0.0513
0.0846 0.0386
0.0027 0.1840
Energy Citrate
rs6940937
GRM1
0.0384
0.0086
7.07E-06
−0.0972
0.0716
0.1745
Unknown X-12627 X-12100
rs7148806 rs7922903
/ SLIT1
−0.0474 −0.0168
0.0107 0.0035
9.78E-06 2.10E-06
−0.0489 0.0723
0.0219 0.0294
0.0257 0.0137
5. Conclusions
Alzheimer's disease, and multiple sclerosis (Moruno-Manchon et al., 2017; Ramesh et al., 2018; van Echten-Deckert et al., 2014). Fingolimod, an S1P receptor modulator, was the first FDA-approved medicine as an orally bioavailable drug for treating relapsing forms of multiple sclerosis (Kihara et al., 2015). Evidence also shows that fingolimod could induce neuroprotective factors in human astrocytes (Hoffmann et al., 2015). Thus, 1-stearoylglycerophosphoethanolamine might provide valuable information for drug discovery for ALS. 2-methoxyacetaminophen sulfate, a xenobiotic that showed causal relationship with ALS, is a member of the class of acetamides. 2-methoxyacetaminophen sulfate can irreversibly bind to GSH, lead to subsequent disruption of the gamma glutamyl cycle, and cause excessive 5oxoproline generation. Citrate is an intermediate metabolite in the tricarboxylic acid (TCA) cycle. Changes in citrate concentration might reflect abnormalities in mitochondrial function, which has also been considered as a causal factor in the pathogenesis of ALS (Chaturvedi and Flint Beal, 2013). We further focused on the potential genetic variants that could determine the variation of the metabolites and ultimately lead to the development of ALS. For example, rs1985243 was the leading SNP (β = −0.0524, SE = 0.0117, P = 7.59E-06) for determining the variation of gamma-glutamylphenylalanine, this SNP also showed causal effects on ALS (β = −0.0878, SE = 0.0377, P = .0198). Based on this fact, we were able to propose the hypothesis that rs1985243 might contribute to the development of ALS through affecting the levels of gamma-glutamylphenylalanine. Although much remains unknown, it can be said that the generated SNPs play important roles in revealing the pathogenesis or therapeutic targets for ALS. Our study also has certain limitations: 1) The metabolites identified for ALS need to be verified by further studies. 2) The database (GDM) used in this study is based on European populations; thus, further studies should be done to include samples with multi-ethnicity to provide a more accurate assessment of the genetic influences on metabolites. 3) We exhibited the genetic variants that might help to elucidate pathophysiological mechanism or therapeutic targets for ALS. However, the biological functions for these variants need to be further explored.
In this study we performed an MR analysis to identify ALS-related metabolites. Our study detected 18 metabolites that might have causal effects on the development of ALS, including a group of gamma-glutamyl amino acids. Our findings suggest that GSH metabolism dysfunction might be involved in the pathogenesis of ALS. A set of genetic variants, such as the SNP rs1985243, which could help inform pathophysiological mechanism or therapeutic targets for ALS, are also shown in our study. Our study provides a novel insight in combining metabolomics with genomics to reveal the pathogenesis and therapeutic strategies for ALS. Declaration of Competing Interest The authors declare no competing financial interests. Acknowledgements We would like to thank the High-Performance Computing Cluster of the First Affiliated Hospital of Xi'an Jiaotong University for data computing. The study was supported by the National Natural Science Foundation of China (81071952). References Baillet, A., Chanteperdrix, V., Trocme, C., Casez, P., Garrel, C., Besson, G., 2010. The role of oxidative stress in amyotrophic lateral sclerosis and Parkinson’s disease. Neurochem. Res. 35, 1530–1537. https://doi.org/10.1007/s11064-010-0212-5. Blasco, H., Garcon, G., Patin, F., Veyrat-Durebex, C., Boyer, J., Devos, D., Vourc’h, P., Andres, C.R., Corcia, P., 2017. Panel of oxidative stress and inflammatory biomarkers in ALS: a pilot study. Can. J. Neurol. Sci. 44, 90–95. https://doi.org/10.1017/cjn. 2016.284. Bowden, J., Davey Smith, G., Burgess, S., 2015. Mendelian randomization with invalid instruments: effect estimation and bias detection through egger regression. Int. J. Epidemiol. 44, 512–525. https://doi.org/10.1093/ije/dyv080. Bowden, J., Davey Smith, G., Haycock, P.C., Burgess, S., 2016. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314. https://doi.org/10.1002/gepi.21965.
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