Causal relationships between DNA methylation、Golgi membrane proteins、endoplasmic reticulum aminopeptidase and abnormal spermatozoa: a two sample Mendelian randomization study

doi:10.21203/rs.3.rs-2209085/v1

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Causal relationships between DNA methylation、Golgi membrane proteins、endoplasmic reticulum aminopeptidase and abnormal spermatozoa: a two sample Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-2209085/v1

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To explore the causal relationship between DNA methylation, Golgi membrane protein, endoplasmic reticulum aminopeptidase, ADP-ribose pyrophosphatase, mitochondrialon abnormal spermatozoa by two-sample Mendelian randomization (TSMR) method.Genetic loci closely related to DNA methylation PhenoAge acceleration, Golgi membrane protein 1, endoplasmic reticulum aminopeptidase 1, endoplasmic reticulum aminopeptidase 2, ADP-ribose pyrophosphatase, mitochondrial were selected as instrumental variables, andTSMR was performed by the inverse variance weighted method, MREgger regression, and weighted median method respectively. Sensitivity analysis was conducted to evaluate the robustness of the MR results.IVW showed that the DNA methylation PhenoAge acceleration(OR = 1.12, 95% CI: 1.01–1.23), Golgi membrane protein 1(OR = 1.22, 95% CI: 1.04–1.44), and endoplasmic reticulum aminopeptidase 2(OR = 1.13, 95% CI: 1.04–1.24) were associated with an increased risk of abnormal spermatozoa. However, there was no evidence of the association between ADP-ribose pyrophosphatase, mitochondrial (IVW OR = 1.25, 95% CI: 0.95–1.63), endoplasmic reticulum aminopeptidase 1 (IVW OR = 1.01, 95% CI: 0.90–1.12) and abnormal spermatozoa. Sensitivity analysis detected little evidence of pleiotropy in the current study.There is a positive causal relationship between DNA methylation PhenoAge acceleration, Golgi membrane protein 1, and endoplasmic reticulum aminopeptidase 2 on abnormal spermatozoa.

Mendelian randomization

DNA methylation

Golgi membrane protein

Endoplasmic reticulum aminopeptidase

mitochondrion

Abnormal spermatozoa

According to a substantial body of research, roughly 15% of couples worldwide experience infertility, with male infertility issues accounting for more than half of cases¹. Male infertility could be attributed to a variety of factors, including poor semen quality. Spermatogenesis is a highly intricate and precise transcription regulation process involving various mechanisms, like typical space-time specific epigenetic regulation mechanism², transcription and post-transcription regulation mechanism³, translation and various modifications of protein⁴, etc. At present, the research focus is on the relationship between the decrease in the semen quality of infertile men and the methylation level of imprinted genes and other genes related to spermatogenesis⁵. However, there is little research on the causal relationship between DNA methylation and other organelle-related proteins on abnormal spermatozoa. Our research aims to find the causal relationship between DNA methylation PhenoAge acceleration, Golgi membrane protein 1, endoplasmic reticulum aminopeptidase 1, endoplasmic reticulum aminopeptidase 2,and ADP-ribose pyrophosphatase, mitochondrial on abnormal spermatozoa by using Mendelian randomization theory.

The basic principle of Mendelian Randomization (MR) is that genetic variants are randomly assigned at conception⁶, which is similar to the process of randomly assigning participants to the treatment group and the control group in a randomized controlled trial. Besides, given that alleles are fixed at birth and cannot be modified by the occurrence or development of diseases, MR design could greatly mitigate reverse causality⁷. MR may avoid some limitations of observational studies (confounding, reverse causality, regression dilution bias) and RCT (representativeness and feasibility issues) in causal inference⁸. With the discovery of a large number of genetic variants closely related to specific traits in biology and the publicly available summary data from genome-wide association studies (GWAS), researchers are allowed to investigate the causal associations between exposures and outcomes of interest⁹. In this study, the two-sample Mendelian randomization method was used to study the causal relationship between DNA methylation PhenoAge acceleration, Golgi membrane protein 1, endoplasmic reticulum aminopeptidase 1, endoplasmic reticulum aminopeptidase 2 and ADP-ribose pyrophosphatase, mitochondrial and the development of abnormal spermatozoa.

2.1 Data source

There are five exposure variables, namely DNA methylation phenoAge acceleration, Golgi membrane protein 1, Endoplasmic reticulum aminopeptidase 2, Endoplasmic reticulum aminopeptidase 1, and ADP-ribose pyrophosphatase, mitochondrial. There is an outcome variable, which is Abnormal spermatozoa. GWAS summary data were all obtained from the IEU consortium(https://gwas.mrcieu.ac.uk/datasets/website).The data were extracted using the corresponding ID, including “ebi-a-GCST90014292” for methylation PhenoAge acceleration, “prot-a-1238” for Golgi membrane protein 1, “prot-a-972” for Endoplasmic reticulum aminopeptidase 2,“prot-a-971” for Endoplasmic reticulum aminopeptidase 1, “prot-a-2129” for ADP-Ribose Pyrophosphase, Mitochondrial, and“finn-b-R18_ABNORMAL_SPERMATOZ” for abnormal Spermatozoa. The above GWAS databases are all from European people, and their brief information is shown in Table 1. Figure 1 shows the study design.

Table 1

Brief description for the genome-wide association study data used in this study
GWAS ID	Year	Trait	Consortium	Sample size	Number of SNPs
ebi-a-GCST90014292	2021	DNA methylation PhenoAge acceleration	NA	34,463	7,545,555
prot-a-1238	2018	Golgi membrane protein 1	NA	3,301	10,534,735
prot-a-972	2018	Endoplasmic reticulum aminopeptidase 2	NA	3,301	10,534,735
prot-a-971	2018	Endoplasmic reticulum aminopeptidase 1	NA	3,301	10,534,735
prot-a-2129	2018	ADP-ribose pyrophosphatase, mitochondrial	NA	3,301	10,534,735
finn-b-R18_ABNORMAL_SPERMATOZ	2021	Abnormal spermatozoa	NA	—	16,380,442

2.2 Instrument selection

To obtain eligible SNPs as instrumental variables, we conducted several filtration steps. First, we extracted the significant SNPs associated with the exposure phenotypes at P < 5 × 10^− 8. Second, we set the linkage disequilibrium (LD) r²at the threshold of 0. 001 within a distance of 10 MB to obtain a set of independent SNPs with minimum P values. Third, we then extracted the exposure SNPs from the outcome data and excluded those significantly associated with the outcome (P < 5 × 10^− 8).Fourth, we harmonized the genetic information of the exposure and outcome to align the SNP effect alleles. SNPs being palindromic or with incompatible alleles were then discarded. Finally, we calculated the F statistics for each SNP. We determined the eligible SNP with strong power using F > 10, which has been described by previous studies. The SNPs finally kept were used for MR analysis.

2.3 Instrumental Variables

After removing the IVs with linkage imbalance, a total of 23 SNPs were included in this study. The basic information of SNPs is shown in Table 2.

Table 2

Basic information of SNPs associated with instrumental variables
Exposure phenotypes	SNP	EA	OA	beta	eaf	se	pval
DNA methylation PhenoAge acceleration	rs11190127	A	C	0.25	0.38	0.04	3.83E-10
DNA methylation PhenoAge acceleration	rs11253338	T	C	0.28	0.18	0.05	8.48E-09
DNA methylation PhenoAge acceleration	rs116853700	A	G	0.55	0.04	0.10	1.59E-08
DNA methylation PhenoAge acceleration	rs1990053	A	G	0.26	0.42	0.04	2.05E-11
DNA methylation PhenoAge acceleration	rs3829957	T	C	-0.38	0.20	0.05	3.51E-15
DNA methylation PhenoAge acceleration	rs6531114	C	T	0.25	0.74	0.04	2.07E-09
DNA methylation PhenoAge acceleration	rs678553	C	T	-0.33	0.31	0.04	2.27E-15
DNA methylation PhenoAge acceleration	rs7228835	C	G	-0.51	0.12	0.06	3.30E-17
DNA methylation PhenoAge acceleration	rs73028070	A	G	-0.43	0.08	0.08	1.74E-08
DNA methylation PhenoAge acceleration	rs752223	A	G	-0.56	0.08	0.07	7.20E-15
DNA methylation PhenoAge acceleration	rs75407001	T	C	-1.12	0.05	0.12	1.34E-21
Golgi membrane protein 1	rs550057	T	C	0.55	0.25	0.03	3.55E-92
Golgi membrane protein 1	rs601338	A	G	-0.27	0.51	0.02	1.12E-28
Endoplasmic reticulum aminopeptidase 2	rs114668143	C	T	-0.93	0.01	0.11	1.66E-18
Endoplasmic reticulum aminopeptidase 2	rs17399025	G	A	-0.30	0.06	0.05	1.26E-08
Endoplasmic reticulum aminopeptidase 2	rs2927608	A	G	1.05	0.44	0.02	1.00E-200
Endoplasmic reticulum aminopeptidase 1	rs1021717	T	G	1.07	0.02	0.10	3.16E-26
Endoplasmic reticulum aminopeptidase 1	rs145753269	T	C	0.70	0.01	0.13	2.57E-08
Endoplasmic reticulum aminopeptidase 1	rs17482078	T	C	-0.96	0.22	0.03	1.00E-200
Endoplasmic reticulum aminopeptidase 1	rs78740770	C	T	0.40	0.04	0.06	9.12E-11
ADP-ribose pyrophosphatase, mitochondrial	rs112635299	T	G	0.47	0.02	0.08	2.04E-08
ADP-ribose pyrophosphatase, mitochondrial	rs1801689	C	A	1.03	0.03	0.07	1.38E-49
ADP-ribose pyrophosphatase, mitochondrial	rs28696943	G	A	-0.36	0.17	0.03	2.29E-28

2.4 Statistical analysis

Inverse-variance weighted (IVW), MR-Egger regression, and weighted median estimator was adopted to examine the causal association between the exposures mentioned above and spermatogenesis(Fig. 2).Results were presented as odds ratio (OR) and corresponding 95% confidence interval (CI), and P < 0.05 was considered statistically significant. Among them, the IVW method was applied as the primary method.IVW requires that the regression line passes through the origin and assumes that all IVs included in the model are valid. The MR-Egger regression is essentially similar to IVW, and the MR-Egger regression assumes that the pleiotropic effect of IVs is independent of the effect of IVs on exposure¹⁰.When effective IVs exceed 50%, the weighted median can obtain a consistent estimation of causal effect, and its efficiency is similar to IVW, and the weighted median is more accurate and effective than MR-Egger regression¹¹.

Cochran’s Q test was used to determine heterogeneity. Besides, we used the intercept of Egger regression to evaluate directional pleiotropy. The “leave-one-out” method was used for sensitivity analysis, and each SNP was successively removed, and the remaining SNPs were used as IVs for MR analysis of two samples, to determine the extent to which the causal association effect was influenced by a single SNP¹².

The “TwoSampleMR” package (version 0.5.6) in R software (version 4.0.3) was used for all the above methods.

A total of 23 single nucleotide polymorphisms were included as instrumental variables, and all the SNPs were statistically sufficient (F > 10).

3.1 Mendelian randomization estimations

IVW showed that the DNA methylation PhenoAge acceleration(OR = 1.12, 95% CI: 1.01–1.23), Golgi membrane protein 1(OR = 1.22, 95% CI: 1.04–1.44), and endoplasmic reticulum aminopeptidase 2(OR = 1.13, 95% CI: 1.04–1.24) were associated with an increased risk of abnormal spermatozoa. The results of MR-Egger regression and weighted median showed consistent results (Table 3;Figure 3)

Table 3

Mendelian randomization results in the current study.
methods	ADP-ribose pyrophosphatase, mitochondrial (3 SNPs)			DNA methylation PhenoAge acceleration (11 SNPs)			Golgi membrane protein 1 (2 SNPs)			Endoplasmic reticulum aminopeptidase 1 (4 SNPs)			Endoplasmic reticulum aminopeptidase 2 (3 SNPs)
methods	OR	95%CI	P	OR	95%CI	P	OR	95%CI	P	OR	95%CI	P	OR	95%CI	P
MR-Egger	1.39	0.69–2.81	0.53	1.12	0.86–1.46	0.41	/	/	/	1.29	0.81- 2.08	0.40	0.96	0.69–1.34	0.84
Weighted median	1.24	0.92- 1.67	0.16	1.05	0.92- 1.20	0.46	/	/	/	1.01	0.90- 1.14	0.84	1.13	1.04- 1.24	0.01
Inverse variance weighted	1.25	0.95- 1.63	0.11	1.12	1.01- 1.23	0.03	1.22	1.04- 1.44	0.02	1.01	0.90- 1.12	0.92	1.14	1.04- 1.24	0.00
Simple mode	1.23	0.86- 1.76	0.38	1.05	0.84–1.30	0.69	/	/	/	1.13	0.93–1.37	0.29	1.05	0.83 1.34	0.72
Weighted mode	1.21	0.85- 1.72	0.40	1.04	0.85–1.26	0.74	/	/	/	1.01	0.89–1.14	0.89	1.13	1.03 1.24	0.12

However, there was no evidence of the association between ADP-ribose pyrophosphatase, mitochondrial(IVW OR = 1.25, 95% CI: 0.95–1.63), endoplasmic reticulum aminopeptidase 1 (IVW OR = 1.01, 95% CI: 0.90–1.12) and abnormal spermatozoa. Similar patterns of association were observed in the weighted median and MR-Egger regression methods.

3.2 Sensitivity analysis

The Cochran Q test of IVW showed that there was no heterogeneity(ADP-ribose pyrophosphatase, mitochondrial: Q = 0.11, P = 0.94; DNA methylation PhenoAge acceleration: Q = 5.38, P = 0.86; Golgi membrane protein 1: Q = 0.32, P = 0.57; Endoplasmic reticulum aminopeptidase 1: Q = 1.64, P = 0.65; Endoplasmic reticulum aminopeptidase 2: Q = 1.35, P = 0.51). Besides, we did not detect any evidence of horizontal pleiotropy based on the intercept analysis of Egger regression (ADP-ribose pyrophosphatase, mitochondrial: intercept = -0.05, P = 0.80; DNA methylation PhenoAge acceleration: intercept = -0.002, P = 0.97; Endoplasmic reticulum aminopeptidase 1: intercept = -0.23, P = 0.40; Endoplasmic reticulum aminopeptidase 2: intercept = 0.17, P = 0.49). The "leave-one-out" sensitivity analysis results showed that after removing each SNP in turn, the IVW analysis results of the remaining SNPs were close to the analysis results of the inclusion of all SNPs, suggesting no SNPs had a strong impact on the estimated causal association.

This two-sample MR study reinforces the evidence for a causal relationship between DNA methylation PhenoAge acceleration and abnormal spermatozoa, Golgi membrane protein 1 and abnormal spermatozoa, endoplasmic reticulum aminopeptidase 2 and abnormal spermatozoa. A total of 23 single nucleotide polymorphisms were included as instrumental variables(Fig. 4). The results of the inverse variance weighted method showed that the DNA methylation PhenoAge acceleration would lead to an increase of 11% in abnormal spermatozoa for every increase of one standard deviation (OR = 1.12, 95% CI: 1.01–1.23). MR-Egger (OR = 1.12, 95% CI: 0.86–1.46) weighted median method (OR = 1.05, 95% CI: 0.92–1.20). The results of the inverse variance weighted method showed that every increase of one standard deviation of Golgi membrane protein 1 would lead to an increase of 22% in abnormal spermatozoa (OR = 1.22, 95% CI: 1.04–1.44). The results of the inverse variance weighted method showed that every increase of one standard deviation of endoplasmic reticulum aminopeptidase 2 would lead to an increase of 13% in abnormal spermatozoa (OR = 1.13 95% CI: 1.04–1.24).

4.1 DNA methylation PhenoAge acceleration on abnormal spermatozoa

As an important epigenetic modification, DNA methylation PhenoAge acceleration is not only closely related to embryo development, gene imprinting, and chromosome inactivation but also plays an important role in gene expression regulation¹³.In mammals, DNA methylation mainly occurs at the 5 '-C site of the cytosine located at 5' -CpG-3 ', resulting in the formation of 5-methylcytosine (5mC). Spermatocytes undergo extensive and specific epigenetic modification and chromatin remodeling to differentiate and produce mature spermatozoa with specific epigenetic profiles¹⁴. Unlike eggs, sperm matures in vivo, and the gene imprinting of mature sperm has been established before fertilization. Therefore, abnormal DNA methylation PhenoAge acceleration can directly affect sperm maturation, embryo development, placental function, and the growth and development of progeny¹⁵.

Excessive ROS(Reactive Oxygen Species)is associated with abnormal DNA methylation¹⁶. A high level of ROS is the most important cause of sperm DNA double-strand breakage and affecting sperm DNA integrity, and thus sperm DNA integrity damage is often called oxidative stress sperm DNA damage¹⁷. It is widely believed that poor sperm DNA integrity can significantly affect reproductive outcomes. DNA methylation PhenoAge acceleration and important gene methylation were found in the sperm of male patients with infertility. It has been reported that low genome-wide DNA methylation levels are associated with sperm motility,¹⁸. ROS accumulation is related to DNA methylation patterns. In particular, DNA damage caused by •OH free radicals affects DNA as a substrate for methyltransferases (Dnmts) and also reduces the acceptance of METHYL groups by DNA bases, thus reducing the overall methylation level of the genome¹⁹. In view of this, we speculate that oxidative stress not only leads to sperm DNA integrity damage but also leads to abnormal sperm DNA methylation PhenoAge acceleration²⁰. Abnormal sperm DNA methylation has been reported in men with low fertility. Meta-analysis showed that abnormal methylation of imprinted genes H19, MEST, and SNRPN was directly related to abnormal semen concentration and motility. In addition to imprinting genes, abnormal methylation of some spermatogenic genes may also lead to impaired fertility²¹. Previous studies have shown that abnormal methylation in promoter regions of MTHFR, RHOX, DAZL, and CREM genes is associated with low routine semen parameters in infertile men. Besides, studies have also shown that decreased sperm quality and abnormal DNA methylation also exist in men with recurrent abortions of unknown causes²². It has been reported that the overall methylation level of sperm also affects the outcome of assisted reproduction, and sperm DNA damage is highly overlapped with the adverse pregnancy outcome caused by abnormal DNA methylation. Given that oxidative stress is the main factor leading to sperm DNA damage, the relationship between the above three factors naturally occurs²³.There are four possible ways to do this.①8-oxodeoxyguanosine (8-oxODG).ROS can directly trigger the oxidation of intracellular macromolecules, and 8-oxODG is the product of DNA oxidative damage²⁴.The presence of 8-oxODG was found to have a negative effect on the DNA methylation PhenoAge acceleration of adjacent sites. In addition, the formation of 8-OXODG affects the binding of hypoxia-inducible factor (HIF-1) to the pro-angiogenic gene VEGF promoter in hypoxic endothelial cells²⁵.②5-hydroxymethylcytosine (5hmC).ROS can hydroxyl 5 mC to form 5hmC. The function of hydroxymethylation is different from that of DNA methylation PhenoAge acceleration²⁶. CpG island methylation suppresses gene expression, while hydroxylation within genes promotes gene expression²⁷. 5 hmC interferes with DNMT1 to block methylation formation, leading to indirect demethylation of CpG sites²⁸.③DNA methyltransferase.ROS can reduce the supply of methyl donor SAM in vivo, thereby limiting the activity of Dnmt and leading to DNA hypomethylation²⁹. It has also been shown that during DNA methylation PhenoAge acceleration, superoxide can deprotonate the cytosine molecule at the c-5 position, thereby accelerating the reaction of DNA with the positively charged intermediate SAM without the need for Dnmt catalysis, but direct evidence of this mechanism has not been determined. In addition, ROS can also induce DNA hypermethylation by increasing Dnmt expression³⁰. ④TET protein. ROS can regulate DNA methylation PhenoAge acceleration by regulating the expression of TET protein. It has been reported that ROS induces an increase in TET1 protein expression and catalyzes the conversion of 5mC to 5hmC, which leads to the demethylation of LINE-1 and several specific genes involved in ROS detoxification and cell cycle arrest³¹.

4.2 Golgi membrane protein 1 on abnormal spermatozoa

Golgi membrane protein 1 (GOLM1) is also known as Golgi protein73 (GP73). The Human GOLM1 gene is located on the short arm of chromosome 9³². Multiple studies have shown that GOLM1 enhances the expression of MATRIX metalloproteinase-13 through transcriptional activation mediated by cAMP response element binding protein, and promotes epithelial cell transformation into mesenchymal cells. Previous studies have found that GOLM1 is a positive regulator of the PI3K/AKT/mTOR pathway and GOLM1 plays a role inregulating mTOR³³. Two classical signaling pathways, P13K-Akt and LKB1 and AMPK regulate the expression of the mTOR signal and its downstream target protein p70S6K / 4EBPl through different molecular pathways. Previous studies have reported that mTOR. P70S6K / 4EBPl signal molecules are involved in the regulation of testicular cell proliferation, differentiation, and spermatogenesis, and the p13K-Akt/LKb1.AMPK³⁴. mTOR signal cascade affects testicular development and spermatogenesis. It was found that the mTOR signaling pathway not only regulates the formation of the blood testosterone barrier but also regulates the opening and closing of the blood testosterone barrier through mTORC1 and mTORC2 respectively³⁵. During spermatogenesis, the mTOR signaling pathway regulates the self-renewal, proliferation, and differentiation of spermatogonial stem cells, and may be involved in the regulation of meiosis of spermatogonial cells. mTOR, p70S6K, 4EBPl, and corresponding phosphorylated proteins P-mTOR, p70S6K, and p-4EBPL were expressed in both spermatogonial stem cells and spermatogonial mother cells, and mTOR signaling plays an important role in phenotypic maintenance and differentiation of early spermatogonial stem cells and spermatogonialcells³⁶. Spermatogenesis includes spermatogonial stem cell proliferation and differentiation, spermatocyte meiosis, and spermatogenesis. mTOR signals can regulate meiosis initiation and proliferation of spermatogonial stem cells and spermatogonial proliferation and differentiation. Inhibition of the mTORCl pathway can induce accumulation of undifferentiated spermatogonial cells in spermatogenic tubules, block spermatogonial differentiation, and inhibit the translation of genes encoding tyrosine kinase receptor C-kit and other cell differentiation regulators in mouse testicular tissues. mTOR signals regulate mRNA translation and editing during spermatogonial differentiation from mouse spermatogonialcells³⁷.

Testicular sertoli cells regulate spermatogenic cell differentiation and sperm maturation through the hormone paracrine. Studies have found that mTORCl can change glycolysis and oxidative stress state in human testicular sertoli cells, and the mTORCl signaling molecule plays an indispensable role in the proliferation, differentiation, and spermatogenic process of testicular spermatogenic cells³⁸. During spermatogenesis, the p70S6K protein in male germ cells of mice is transferred from the nucleus to the cytoplasm. We can control the generation and proliferation of male germ cells by regulating the expression of the p70S6K protein. SCF/C-Kitis an important regulator of human cell growth, differentiation, and development. C-Kit is mainly expressed in spermatogonial TYPE A cells in testis. SCF/C-Kit can enhance the phosphorylation level of p70S6K in spermatogonial cells, increase the expression of CyclinD3 protein, and promote cell cycle progression³⁹.

At present, mTOR. P70S6K / 4EBPl signal plays an important role in testicular development and spermatogenesis, but the research on its regulation of testicular spermatogenesis and its mechanism is still in the exploratory stage and the related issues of mTOR. P70S6K / 4EBPl signal and cell proliferation and differentiation during testicular development need further study⁴⁰. The signaling pathways mediated by LKBL-AMPK and P13K-Akt are not independent of each other, but coordinate with each other to regulate cell metabolism and other life processes. The p13K-Akt/LKBL. AMPK signaling cascade regulates mTOR, and the details of how it responds to environmental stimuli and maintains normal testicular spermatogenic function are not well understood⁴¹. With further research on related mechanisms, p13K. Akt/LKBL. The AMPK-mTOR signaling pathway is expected to become a central regulatory link of spermatogenesis⁴².

4.3 Endoplasmic reticulum aminopeptidase 1 and endoplasmic reticulum aminopeptidase 2 on abnormal spermatozoa.

The polymorphism of ERAP2 is lower than that of ERAP1, and the frequency of distribution of ERAP2 and ERAP1 is approximately equal in the population. ERAP1 and ERAP2 may function separately, but about 30% of their molecules form heterodimers that may digest substrates faster than individual ERAPs.

A previous study found that 25 percent of healthy individuals lacked the functional ERAP2 protein, while the immune systems coped well without ERAP2, suggesting that it was largely unimportant and only played a supporting role for ERAP1⁴³.Studies have confirmed that ERAP2 can bind to ERα as a coactivator to increase H3K4, H3K36me2, and H3K36me3 trimethylation levels and participate in the regulation of ERα target gene transcription. Studies have shown that WHSC1 is associated with diseases that affect growth and development and plays a role in DNA damage responses⁴⁴. Dimethylation of H3 at lysine 36 (H3K36me2) is the main chromatin regulatory activity of WHSC1, which may regulate spermatogenesis gene expression through catalytic H3K36me2 modification. During spermatogenesis, H3K36me2 and H4K16ac may have a reverse regulatory effect on sperm histone conversion to protamine, and WHSC1 knockout may lead to sperm acrosome and other morphological abnormalities⁴⁵. WHSC1 also participates in trimethylation of H3K36 to regulate RNA splicing and DNA damage signaling pathways. Loss of the SETD2 factor that mediates H3K36me3 modification leads to abnormal spermatogenesis and acrosomal malformation prior to step 8 of spermatogenesis, leading to infertility(46).

4.4 ADP-ribose pyrophosphatase, mitochondrial on abnormal spermatozoa

PRPS2(pyrophosphatase2)is specifically highly expressed in human, mouse, and rat testicular tissues, and is related to the inactivation of the X chromosome, and participates in the regulation of purine and pyrimidine nucleotide metabolism(47). Previous studies have shown that the expression of PRPS2 in testicular tissues of patients with Sertoli syndrome is significantly higher than that in testicular tissues with normal spermatogenic function, which is involved in the development of Sertoli syndrome(48). Studies have shown that PRPS2 was positively expressed in the nucleus and cytoplasm of spermatocytes, spermatocytes, sertoli cells, and stromal cells, while no significant positive expression was observed in sperm cells and mature sperm, suggesting that PRPS2 is involved in the normal spermatogenesis of the testis, and no causal relationship between PRPS2 and abnormal spermatozoa was found(49).Although we used MR analysis, the study still had some limitations :(1) the study included relatively few SNPs as IV, which had limited ability to detect causal associations; (2) Some unknown confounding factors, including those not yet reported in the literature, cannot be completely excluded; (3) The population included in the study was mainly European. Since the results of causal association analysis may also be affected by ethnicity, we may need to conduct the same MR study in other ethnic groups to verify the conclusion. (4) The study could not obtain the data of each patient, so further subgroup analysis could not be performed in this study.

In conclusion, two-sample Mendelian randomization revealed a causal association between DNA methylation PhenoAge acceleration, Golgi membrane protein 1, and endoplasmic reticulum aminopeptidase 1on abnormal sperm, but no causal association between endoplasmic reticulum aminopeptidase 2 and ADP-ribose pyrophosphatase, mitochondrialonabnormal spermatozoa.

Data sharing

The datasets generated and/or analysed during the current study are available in the [GWAS summary data] repository,[https://gwas.mrcieu.ac.uk/datasets/website]

Ethics statement

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study doesn’t include human and animal experiments.

Consent to participate

Not applicable

Competing Interests

All authors declare no competing financial interests.

Funding

This study was supported by the scientific research project of Shanxi Provincial Health Commission, (No. 2022073)

Author Contributions

WBS carried out the genetic studies, participated in the proteomic analysis and drafted the manuscript. LP and ZJD carried out the immunoassays and performedthe statistical analysis. HQC, FL, MW conceived of the study, and participated in its design and coordination and helped to draftthe manuscript. All authors read and approved the final manuscript.

Acknowledgement

We would like to acknowledge the reviewers for their helpful comments on this paper.

Ethics of approval statement

Due to such a re-analysis of previously collected and published data, no additional ethics approval was needed.

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Causal relationships between DNA methylation、Golgi membrane proteins、endoplasmic reticulum aminopeptidase and abnormal spermatozoa: a two sample Mendelian randomization study

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Data source

2.2 Instrument selection

2.3 Instrumental Variables

2.4 Statistical analysis

3. Results

3.1 Mendelian randomization estimations

3.2 Sensitivity analysis

4. Discussion

4.1 DNA methylation PhenoAge acceleration on abnormal spermatozoa

4.2 Golgi membrane protein 1 on abnormal spermatozoa

4.3 Endoplasmic reticulum aminopeptidase 1 and endoplasmic reticulum aminopeptidase 2 on abnormal spermatozoa.

4.4 ADP-ribose pyrophosphatase, mitochondrial on abnormal spermatozoa

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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