Investigating the Correlation between Genetic and Biochemical Markers in Multiple Sclerosis

doi:10.21203/rs.3.rs-2627617/v1

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Investigating the Correlation between Genetic and Biochemical Markers in Multiple Sclerosis

https://doi.org/10.21203/rs.3.rs-2627617/v1

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Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system with a complex etiology involving genetic and environmental factors. Genome-wide association studies have identified several genetic variants associated with MS susceptibility, including the XRCC1 gene involved in DNA base excision repair. Environmental factors such as exposure to nitric oxide have also been linked to MS development and progression. This study aims to investigate the potential association between these factors, including patient demographic descriptors and biochemical markers, to better understand the mechanisms underlying MS. This scientific article discusses the results of statistical analysis of biomarkers in relation to multiple sclerosis (MS). Our findings reveal significant associations between various factors, such as levels of nitric oxide (NO) and serum nitrite levels, NO and plasma malondialdehyde (MDA) levels, nitrite and nitrate levels in serum, and nitrite and MDA levels in plasma. It also suggests a potential association between XRCC1 rs25487 T > C locus deviation from Hardy-Weinberg equilibrium and MS in the population of Latvia. Furthermore, XRCC1 rs25487 T > C is found to be statistically significantly associated with DNA damage in peripheral blood mononuclear cells (PBMNC) and potentially associated with levels of NO. Additionally, PARP1 rs1136410 A > G is linked to plasma levels of MS patients. Finally, the study finds that XRCC1 rs25487 T > C is associated with both plasma nitrite levels and serum malondialdehyde (MDA) levels of MS patients, with the rare allele T specifically correlated with the latter.

genotyping

biomarkers

XRCC1

correlation

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS) that affects approximately 2.8 million people worldwide [1]. MS is characterized by inflammation, demyelination, and axonal damage in the CNS, leading to various neurological symptoms and disability [2]. Despite significant advancements in the understanding of MS pathogenesis, the exact etiology of the disease remains unknown. It is believed that a combination of genetic and environmental factors contributes to the development of MS [3].

Genome-wide association studies (GWAS) have identified several genetic variants associated with MS susceptibility [4]. In particular, the XRCC1 gene has been implicated in the development and progression of MS. XRCC1 encodes a protein involved in base excision repair (BER), a pathway that repairs damaged DNA by removing damaged bases and replacing them with new ones [5].

In addition to XRCC1, several other DNA repair genes have been implicated in the pathogenesis of MS. The ATM gene, which encodes a protein involved in DNA double-strand break repair, has been shown to be associated with an increased risk of MS [6]. The involvement of the PARP1 gene in the development and progression of MS is currently unknown, however, PARP1 mediated DNA repair pathways function in synergy with ATM gene [7]. Additionally, the XPA and XPC genes, which are involved in nucleotide excision repair, have also been linked to MS susceptibility [6].

In addition to genetic factors, environmental factors such as exposure to nitric oxide (NO) have been implicated in the development and progression of MS [3], [8]. NO is a reactive nitrogen species that plays a role in various physiological processes, including vasodilation, neurotransmission, and immune response [9]. However, excessive production of NO has been shown to induce DNA damage and contribute to the pathogenesis of various diseases, including MS [10].

Given the importance of DNA repair genes, DNA damage, and oxidative stress markers in the pathogenesis of MS, it is essential to investigate the potential association between these factors. This study aims to investigate the association between ATM, PARP1, XPA, XPC and XRCC1 gene SNPs, patient demographic descriptors, and biochemical markers. The findings of this study may contribute to a better understanding of the mechanisms underlying the development and progression of MS and may have implications for the development of novel therapeutic strategies for the disease.

Patients

A cohort study was conducted, comprising 96 patients who underwent SNP genotyping to investigate the role of genetic markers in multiple sclerosis (MS). Of these patients, 26 were previously included in a biochemical analysis study [11], [12]. The study group consisted of 78.1% females and 21.9% males, reflecting the well-known female predisposition to MS [13]. The RRMS subtype was present in 43.8% of patients, while 56.2% had progressed to the SPMS stage. The average duration of RRMS was 13.3 ± 8.2 years, and the average duration of MS in the entire study group was 29.2 ± 9.3 years. The study group had an average age of 50.7 ± 10.7 years, with a median value of 51.5 and an interquartile range (IQR) of 16 years. The mean disease duration for the study group was 21.5 ± 8.9 years, with an IQR of 10.8 years. The majority of patients (74.0%) were undergoing disease-modifying therapy (DMT), with 34.4% administering interferon-based drugs. For a more detailed breakdown of the study group refer to Table 1.

Table 1

Demographic and clinical characteristics of patients with MS in study groups
Descriptor	Genotyping	Biochemistry
Sample count	96	26
Sex:
Females	75 (78.13)	18 (69.23)
Males	21 (21.88)	8 (30.77)
Age of diagnosis, years:
Mean ± SD	29.23 ± 9.26	29.04 ± 8.32
Median with IQR	29 and 13	29 and 10.5
Age, years
Mean ± SD	50.73 ± 10.68	48.65 ± 12.43
Median with IQR	51.50 and 16.00	48.50 and 22.75
Disease duration, years:
Mean ± SD	21.50 ± 8.92	19.62 ± 8.06
Median with IQR	20 and 10.75	19 and 9.5
EDSS:
Mean ± SD	4.83 ± 1.94	4.63 ± 1.74
Median with IQR	5 and 3.5	4.5 and 3.5
MS type:
RR	42 (43.75)	12 (46.15)
SP	54 (56.25)	14 (53.85)
RR duration for SP patients:
Mean ± SD	13.30 ± 8.19	10.36 ± 6.83
Median with IQR	11.5 and 12.25	10 and 7.75
Brain lesions (49 patients):
Yes	49 (100.00)	17 (100.00)
No	-	-
Spinal cord lesions (49 patients):
Yes	47 (95.92)	16 (94.12)
No	2 (4.08)	1 (5.88)
DMT:
Yes	71 (73.96)	18 (69.23)
No	25 (26.04)	8 (30.77)
DMT type:
No	25 (26.04)	8 (30.77)
IFN	33 (34.38)	9 (34.62)
Glatiramer acetate (GA)	9 (9.38)	2 (7.69)
Mitoxantronum (M)	17 (17.71)	3 (11.54)
Other	12 (12.50)	4 (15.38)

EDSS Expanded Disability Status Scale, IQR interquartile range, RRMS relapse-remitting multiple sclerosis, SPMS secondary progressive multiple sclerosis, DMT disease modifying therapy, IFN interferon, SD standard deviation

Biochemical markers

DNA damage, nitric oxide production, levels of nitrites and nitrates, and MDA levels were measured in this study to investigate potential biomarkers in a disease context. Alkaline comet assay was used to identify DNA damage, while EPR was utilized to detect nitric oxide production. Nitrites and nitrates in plasma and serum were measured using the Sievers NOA-280i chemiluminescence detector, and MDA levels in plasma and serum were assessed via TBARS assay. Detailed methods and results for these assays can be found in previous studies [11], [12].

Genotyping

Genotyping was performed using the Primer3 software for primer design, followed by cleaved amplified polymorphic site method. Table 2 displays the relevant primer sequences and restriction methods used for genotyping. Note that fragment sizes in bold (227 + 215 for rs189037 and 267 + 247 for rs1800975) could not be distinguished on the gel. If a band was visible in the appropriate size region, both fragments were assumed to be present.

Table 2

Primers for PCR amplification and restriction conditions
Gene ID	Marker ID	Restrictase	Primer	Product size (bp), amplified/restricted
ATM	rs189037	MlsI	F-5' - ACTGTCGTCACTTCCGTCCT-3'	575/442 + 227 + 215 + 133
ATM	rs189037	MlsI	R-5' - TGAGGAGAGGGAGGAGTCAA-3'	575/442 + 227 + 215 + 133
PARP1	rs3219090	AgsI	F-5' - GAAAAGACTGTTCCTCCTCGAT-3'	689/638 + 381 + 257 + 51
	rs3219090	AgsI	R-5' -CTGGAACAGATGCCGTCCAA-3'	689/638 + 381 + 257 + 51
	rs1136410	EciI	F-5' - AAGTTCACGCTACAAGGGTTT-3'	626/626 + 477 + 149
	rs1136410	EciI	R-5' -CTGACCAGCAGGAGGGTTTG-3'	626/626 + 477 + 149
XPA	rs1800975	ApaI	F-5' -AGGTGACCAGGTCGTGAGATA-3'	514/514 + 267 + 247
XPA	rs1800975	ApaI	R-5' -GCTTGCACGAGCCAGTCT-3'	514/514 + 267 + 247
XPC	rs2228001	PvuII	F-5' -GGACGGCTCAGTAAGAAGGG-3'	792/792 + 407 + 385
XPC	rs2228001	PvuII	R-5' -AAACGATCAGCGCATTCACG-3'	792/792 + 407 + 385
XRCC1	rs1799782	PvuII	F-5' -CCAAAACCCACCAGTGATCCA-3'	622/622 + 376 + 246
	rs1799782	PvuII	R-5' -ATGGGTGTTCATGAGAGGGAAG-3'	622/622 + 376 + 246
	rs25487	PvuII	F-5' -GCTCCTCCAGCCTTTTCTGA-3'	688/688 + 449 + 239
	rs25487	PvuII	R-5' -CTCATGTAGGCTTGCGCC-3'	688/688 + 449 + 239

DNA digestion with restriction enzymes was carried out in accordance with the manufacturer protocols (Fermentas, Vilnius, Lithuania), and products of PCR amplification and enzyme restriction were analyzed by gel electrophoresis in 1–5% agarose gel. Gels were stained with ethidium bromide and visualized using a UV camera.

Statistical analysis

Statistical analysis was conducted using the IBM SPSS Statistics v25.0 software. Shapiro-Wilk or Kolmogorov-Smirnov tests were used to assess the normality of numerical data. For sample pools with n < 50, the Shapiro-Wilk test was applied, while for n > 50, the Kolmogorov-Smirnov test was used. One-way ANOVA was performed for group-wise comparisons of data with more than two groups, while parametric t-tests were used for data with two groups that followed a normal distribution. The Mann-Whitney test was used for two-group comparisons of data that were not normally distributed, and the Kruskal-Wallis test was used for comparisons of multiple groups.

Before correlation analysis, the Hardy-Weinberg equilibrium was calculated for each SNP. Pearson r and Spearman ρ tests were used to estimate the correlation between biochemical and genetic biomarkers for data following and outside normal distribution, respectively.

Correlation between biochemical markers

In terms of the results, the correlation analysis of biochemical markers revealed significant findings for some markers. Specifically, significant medium reverse correlations were observed between levels of NO and serum nitrite levels (p = 0.022) and plasma MDA levels (p = 0.0049), indicating that an increase in NO levels was followed by a decrease in MDA levels in plasma, and vice versa. A significant medium reverse correlation was also observed between nitrite and nitrate levels in serum (p = 0.0013; correlation coefficient − 0.61). Another significant correlation was found between plasma MDA levels and serum nitrite levels (p = 0.0026). In addition, two correlations were close to significance: plasma and serum nitrate levels (p = 0.035) and serum MDA levels and serum nitrate levels (p = 0.084). A heatmap of these correlations is provided in Fig. 1.

DNA repair gene ATM, PARP1, XPA, XPC and XRCC1 SNP genotyping

SNP genotyping was conducted for seven loci of five DNA repair genes and the results are presented in Table 3. Significant deviation from HW equilibrium was observed for XRCC1 rs25487 T > C (PHW = 0.000213). Our findings suggest that Latvian MS patients have a dominant amount of rare allele T (MAF = 0.66) compared to NCBI data (T = 0.3658 according to 1000Genome and T = 0.357623 according to NCBI European).

Table 3

MS collection SNP genotyping result
SNP	MS collection			Statistical analysis between sexes
	Frequency, % (sample number)			Statistical analysis between sexes
	All samples	Females	Males	P_χ	V₁
ATM rs189037 G > A
N	96	55	21	-	-
MAF	49.48	51.33	42.86	0.33	0.07
AA	28.13 (27)	26.67 (20)	33.33 (7)	0.07	0.1
AG	44.79 (43)	44.00 (33)	47.62 (10)
GG	27.08 (26)	29.33 (22)	19.05 (4)
P_HW	0.65	0.65	1	-	-
PARP1 rs1136410A > G
N	92	22	20	-	-
MAF	19.02	19.44	17.5	0.78	0.02
AA	69.57 (64)	69.44 (50)	70.00 (14)	1	0.06
AG	22.83 (21)	22.22 (16)	25.00 (5)
GG	7.60 (7)	8.34 (6)	5.00 (1)
P_HW	2.53 x10-2	0.07	0.92	-	-
PARP1 rs3219090C > T
N	90	33	20	-	-
MAF	31.11	32.67	35	0.55	0.04
CC	51.11 (46)	52.86 (37)	45.00 (9)	0.77	0.06
CT	35.56 (32)	34.28 (24)	40.00 (8)
TT	13.33 (12)	12.86 (9)	15.00 (3)
P_HW	0.29	0.27	0.69	-	-
XPA rs1800975 T > C
N	96	41	21	-	-
MAF	31.25	32.67	26.19	0.61	0.04
CC	46.88 (45)	45.33 (34)	52.38 (11)	0.88	0.06
CT	43.75 (42)	44.00 (33)	42.86 (9)
TT	9.38 (9)	10.67 (8)	4.76 (1)
P_HW	0.99	1	0.95	-	-
XPC rs2228001 G > T
N	92	41	17	-	-
MAF	35.87	33.33	47.06	0.13	0.11
TT	40.22 (37)	45.33 (34)	17.65 (3)	0.28	0.15
TG	47.83 (44)	42.67 (32)	70.59 (12)
GG	11.96 (11)	12.00 (9)	11.76 (2)
P_HW	0.9	0.91	0.15	-	-
XRCC1 rs1799782G > A
N	96	10	21	-	-
MAF	7.81	6.67	11.9	0.26	0.08
GG	84.38 (81)	86.67 (65)	76.19 (16)	0.24	0.12
GA	15.62 (15)	13.33 (10)	23.81 (5)
AA	0 (0)	0 (0)	0 (0)
P_HW	0.658	0.74	0.62	-	-
XRCC1 rs25487 T > C
N	96	72	21	-	-
MAF	54.17	53.33	57.14	0.66	0.03
CC	10.42 (10)	13.33 (10)	0 (0)	0.17	0.2
CT	70.83 (68)	66.67 (50)	85.71 (18)
TT	18.75 (18)	20.00 (15)	14.29 (3)
P_HW	2.13 x 10^− 4	1.40 x 10^− 2	1.76 x 10^− 3	-	-

MAF minor allele frequency, P_HW statistical significance between expected and real genotype distribution according to Hardy-Weinberg equilibrium. Common alleles in bold.

Correlation of SNPs with biochemical parameters

The statistical analysis revealed that XRCC1 rs25487 T > C SNP significantly correlates with DNA damage in PBMNC with a p value of 0.0093. Patients with the rare homozygous genotype TT have approximately two times greater DNA damage compared to CC and CT genotypes. The XRCC1 rs25487 SNP also showed a close to statistically significant correlation with levels of NO (p = 0.079), serum nitrites (p = 0.05), plasma MDA levels (p = 0.059), and serum MDA levels (p = 0.093). Interestingly, levels of NO follow a trend similar to DNA damage and are elevated in patients with the T allele present in the genotype. However, serum nitrite levels tend to be higher if the C allele is present in the genotype. Plasma MDA levels show about 1.5 times decrease if at least one rare T allele is present in the genotype. As for serum MDA levels, it appears that the common allele C affects the increase. Another SNP rs1799782 G > A of the same XRCC1 gene showed a close to significant correlation with plasma nitrate levels. Although the common genotype GG had slightly increased nitrate levels, a larger sample pool is required for a definitive conclusion.

Furthermore, the PARP1 rs11364110 A > G SNP indicated a close to significant correlation with plasma nitrite levels (p = 0.046). Patients with rare GG genotype had notably increased nitrite levels in plasma. Detailed results are presented in Supplementary Tables and Fig. 2.

Nitrites and nitrates are commonly measured in both plasma and serum using various analytical methods. However, the presence of interfering components in the past has made these investigations difficult [14]. To address this, our study utilized the chemiluminescence detection method, which has been shown to facilitate the detection of nitrites and nitrates while excluding many interfering factors [15]. However, this method requires the removal of plasma and serum proteins. Given that plasma protein content is 3–5 g/L higher than serum protein content (Blood Proteins - an overview | ScienceDirect Topics), our positive correlations between serum nitrites and nitrates with other parameters and lack of correlation for plasma measurements could be explained.

Our findings suggest that serum nitrate and nitrite measurements may be more informative, with correlations previously found between NO metabolites and clinical activity [17] and disease severity as measured by the Expanded Disability Status Scale (EDSS) in some studies [18] being supported by our results. We observed correlations between serum nitrates and gender as well as EDSS score of patients, with females being more susceptible to MS. However, the relatively small size of our study group, resulting in a low female to male ratio, limited our ability to observe any statistical significance.

The biochemical markers chosen for our study are influenced by various environmental and dietary factors that are difficult to account for. However, if any of the biochemical markers, such as nitrate levels, correlate with the EDSS score, it could provide a valid point to assume that the given biomarker might be associated with the course of the disease progression. In our case, serum nitrate levels had a reverse correlation with the EDSS score, suggesting that patients with more severe cases of MS are expected to have decreased nitrate levels. However, due to the limitations of our study, it is currently hard to tell whether this correlation is accidental or consequential.

It has been previously reported that MS patients receiving interferon therapy exhibit higher levels of NO metabolites [8], which could be the cause of the increase in NO production revealed in our study. However, our correlation results did not show any significant association between NO, nitrites, and nitrates with disease-modifying therapy (DMT).

Although we did not find any associations of MDA levels with DMT or patient descriptors, serum MDA levels were close to significance when correlated with serum nitrates. To date, no studies have proven an association between NOx and MDA in other cases [19], [20]. A study by Violi et al. [21], which is rather ancient, is by far the only theory explaining the reverse correlation between NO metabolite levels and MDA levels. In our case, we observed a medium reverse correlation between NO levels, serum nitrate and nitrite levels, and MDA levels. Based on evidence that NO can also act as an antioxidant in lipid peroxidation, one might speculate that the overproduction of NO, and thus a higher level of its metabolites, as shown in this study, could result in lower levels of MDA. However, both nitric oxide metabolite and MDA levels are highly dependent on dietary antioxidant intake and lifestyle in general. Thus, a larger study group would be required to minimize the deviation of each sample from one another. Further investigation is required.

The XRCC1 gene SNP rs25487 has been found to deviate significantly from the Hardy-Weinberg equilibrium (HWE). Prior studies have suggested that this SNP is linked to a reduced risk of minor treatment response in esophageal cancer and an increased risk of high-grade side effects in head and neck cancer [22]. Given the increased levels of DNA damage observed in leukocytes of patients with multiple sclerosis (MS), it is possible that this SNP is also associated with DNA lesions in MS. Correlation analysis has demonstrated a significant association between XRCC1 rs25487 and DNA damage in MS, indicating that this SNP may indeed be linked to DNA damage in MS. Interestingly, XRCC1 rs25487 also exhibited a close to significant correlation with nitric oxide (NO) levels, serum nitrites, and MDA levels in both plasma and serum. However, this correlation requires further investigation as no studies have confirmed this link.

It is currently unclear whether the mutation in XRCC1 gene causes fluctuations in NO levels or if NO is inducing genotoxicity and causing point mutations. A study by Kim, 2017 found that NO has mutagenic properties and is capable of producing point mutations in certain genes. One example involved the mutagenic potency of NO produced by interferon gamma (IFN-ɣ) activated macrophages, which may be relevant to the MS case. Therefore, it is possible that NO may be the reason behind the XRCC1 mutation.

As expected, XRCC1, being a DNA repair gene, shows its SNP rs25487 association with levels of DNA damage in lymphocytes. The rare genotype TT has been found to correlate with DNA lesions in MS patients, suggesting that the C:T point mutation in XRCC1 may be caused by the mutagenic properties of NO, disrupting the XRCC1 DNA repair pathway and resulting in more evident DNA damage. However, DNA lesions can be influenced by numerous environmental factors, so additional and more specific investigations with a large sample pool are required to confirm this hypothesis.

Statistical analysis of biomarkers has revealed significant associations between various factors, including levels of NO and serum nitrite levels, levels of NO and plasma MDA levels, nitrite and nitrate levels in serum, and nitrite and MDA levels in plasma. Additionally, the XRCC1 rs25487 T > C locus deviation from Hardy-Weinberg equilibrium suggests a potential association with MS in the population of Latvia. Furthermore, XRCC1 rs25487 T > C demonstrates a statistically significant association with DNA damage in PBMNC, and is potentially associated with levels of NO. PARP1 rs1136410 A > G is linked to plasma levels of MS patients, while XRCC1 rs25487 T > C is associated with both plasma nitrite levels and serum MDA levels of MS patients, with the rare allele T being specifically correlated with the latter.

Funding

This research was supported by the European Regional Development Fund under the project “Determination of proteasome-related genetic, epigenetic and clinical markers for multiple sclerosis” (Project No. 1.1.1.1/16/A/016).

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study conception and design. All experiments were performed and the first draft of the manuscript was written by Vitalijs Borisovs. Statistical analysis was performed by Ilva Trapina. Materials, samples and equipment were provided by Natalia Paramonova. The study was designed and supervised by Natalia Paramonova and Nikolajs Sjakste.

Data availability

All the relevant data are described in the manuscript and/or in supplementary materials. Additional supporting materials are available from the corresponding author on reasonable request.

Ethics approval

Permission of the Central Medical Ethics Committee of the Republic of Latvia No 1/17–10-10 (issued 10.10.2017) was received to perform this study.

Consent to participate

Informed consent was obtained from all individual participant included in the study.

Consent to publish

The authors affirm that human research participants provided informed consent for publication according to personal data safety and GDPR guidelines.

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Investigating the Correlation between Genetic and Biochemical Markers in Multiple Sclerosis

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Introduction

Materials And Methods

Patients

Biochemical markers

Genotyping

Statistical analysis

Results

Correlation between biochemical markers

DNA repair gene ATM, PARP1, XPA, XPC and XRCC1 SNP genotyping

Correlation of SNPs with biochemical parameters

Discussion

Conclusions

Declarations

References

Supplementary Files

Status:

Version 1