Initial evaluation of LncRNA A2M-AS1 gene expression in multiple sclerosis patients

doi:10.21203/rs.3.rs-3238115/v1

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Initial evaluation of LncRNA A2M-AS1 gene expression in multiple sclerosis patients

https://doi.org/10.21203/rs.3.rs-3238115/v1

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Introduction: Multiple sclerosis (MS) is one of the three leading neurodegenerative diseases worldwide. Genes expression profiles studies play important role in recognition and prevention of disease. Considering the inherent ability of biomarkers to diagnose and prognosis the occurrence of a disease, with the aim of gene therapy and changing gene expression, it can be helped to treat it. In this study, by examining the gene interaction and expression of non-coding gene in patients with multiple sclerosis, using bioinformatics analyzes and laboratory research, to find the gene expression pattern and the interaction of potential biomarkers of this disease for It was tried to find suitable treatment targets.

Materials and methods: First, by using microarray data analysis of GEO database, the expression status of Lnc RNA A2M-AS1 gene was investigated in patients with MS. Then lncRNA-mRNA interaction analysis was performed in the lncRRisearch and ENCORI database. After sample collection, the total RNA extracted using the RNA extraction kit from 20 patient samples and 20 healthy samples was synthesized into cDNA with the synthesis kit. In the following, two pairs of forward and reverse primers were designed for A2M-AS1 gene, and finally, the expression level was measured by Real Time-PCR technique.

Result: Based on bioinformatic and laboratory analysis, the expression of A2M-AS1 gene in MS samples showed a significant decrease in expression compared to healthy samples. Also, based on the ROC analysis, lncRNA A2M-AS1 can be introduced as an acceptable diagnostic biomarker to distinguish MS samples from healthy samples.

Conclusion: lncRNA A2M-AS1 by reducing its expression as an acceptable diagnostic biomarker can increases the risk of developing MS.

Multiple sclerosis

gene expression changes

A2M-AS1

biomarker

Neurodegenerative diseases (NDDs) are wide range of diseases that initially involve the neurons of the brain and, over time, the process of progressive damage to the nerves leads to the gradual loss of the structure or function of the neurons and finally the apoptosis of the central nervous cells. Neurodegenerative diseases can affect a person's movements, speech, memory, intelligence and other things. These diseases are very complex, the exact cause of many of them is still unclear, and because there is no known way to reverse the progressive degeneration of neurons, they are considered incurable.(1)

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system that is caused over time by autoimmune invasion and progressive destruction of the myelin sheath of nerve axons. While the cause is unclear, genetics and environmental factors are proposed as causes(2, 3).Specific genes that have been linked with MS include differences in HLA system on chromosome 6 that serves as MHCs. The most consistent finding is the association between multiple sclerosis and alleles of the MHC defined as DR15 and DQ6. Other loci have shown a protective effect, such as HLA-C554 and HLA-DRB1*11(4). To help early diagnosis and prognosis of MS, vitamin D dose(5), pathobiological tests of serum or blood-brain barrier fluid and ELISA(6) ,interpretation of demographic data (7), analysis of neuroimaging data with machine learning (8), MRI (9) and molecular and genetic biomarkers(6) play an emergent role. Biomarkers are biological molecules present in all body tissues that indicate a normal or abnormal process, or a condition or a disease. In various cancers and neurodegenerative diseases, at the genetic level, which is called a genetic marker, it is a DNA sequence that causes or aggravates the disease or is related to the diagnosis and declaration of susceptibility to a specific disease(6). The given biomarker in this study was Long non-coding RNA A2M-AS1. LncRNA is transcripts longer than 200 nucleotides that are not translated into protein(10). LncRNA A2M-AS1 is the number one antisense RNA of the protein coding A2M gene (alpha-2-macroglobulin), which has the highest expression level in normal lung and liver cells, and is an almost innovative LncRNA in the field of research, which is believed to play a role in cancer pathways. The product of this gene is a protease inhibitor and cytokine transporter that inhibits a wide range of proteases. Mutations cause alpha-2-macroglobulin deficiency(11). In this research, based on investigating the mechanism of effect and interaction of long non-coding RNA A2M-AS1 with other genes in patients with multiple sclerosis, it is hoped to find a new biomarker in the diagnosis and prognosis of this disease.

Gene expression analyzes

Gene expression analyzes performed based on microarray data obtained from the GEO database and RNA sequencing data in study of GSE43591 to measure and find genes with expression changes (DEGs) in 20 samples of MS patients in comparison to 20 control samples(12).The platform used in this experiment, which is also known as GPL520, is as follows: [HG-U133_Plus_2] Affymetrix Human Genome Array U133 Plus 2.0. DEG analysis was performed by using affy and limma packages, which were downloaded from the Bioconductor database. Microarray analysis graphs were drawn using the ggplot2 package, where genes with 1 > Log FC and 1 < Log FC were identified as significant DEGs with a significance level of adjusted P. Value less than 0.05. All stages of DEG analysis were performed on Microarray data and corresponding graphs were drawn using the Studio R (Version 4.1.2) programming language.

Sampling

This case-control study was conducted without the direct presence of patients and on 40 blood samples obtained from people with MS and healthy people, according to the modified criteria of McDonald. Healthy samples of volunteers without any inflammatory or autoimmune disease according to gender, age and ethnicity were selected as the control group.

RNA extraction

Total RNA of blood samples was extracted by using the RNX-plus extraction kit. After Removal of genomic DNA in the extracted RNA, The RNA concentration was estimated by using Nanodrop Spectrophotometer (ND-1000, ThermoFisher, MA, USA). Purified RNA was stored at -80˚C for further steps.

cDNA synthesis and Real-Time PCR

Due to the instability of RNA, in order to measure the expression level of the target genes, we must use the reverse transcription technique (Real-Time PCR). In fact, the reverse transcription reaction is a reaction in which the RNA-dependent DNA polymerase enzyme converts RNA into cDNA. Treated RNA with DNase turned into cDNA by Universal cDNA Synthesis Kit (Roje, Iran/Tehran) based on the manufacturer’s protocol. The synthesized cDNAs were stored at -20˚C until the polymerase chain reaction (PCR). All reactions were carried out in double. qRT-PCR data were assessed according to the -ΔΔCT method. The corresponding lncRNA Ct values were normalized against GAPDH, as a reference gene.

Statistical analysis

Statistical analysis of real-time PCR datas and graphing performed by Graph Pad Prism software (Version 9.0.0) Kolmogorov-Smirnov test has used to measure the normality of the expression data distribution. Paired and unpaired t-test were used to compare and analyze the difference in the expression of LncRNA A2M-AS1 between control and MS samples. Receiver Operating Characteristic (ROC) analysis and drawing of ROC diagram was done by using GraphPad prism software. This test is used to measure the biomarker potential of candidate gene based on two factors including Area Under Curve (AUC) and P. Value. The Mann-Whitney U test was carried out to analyze the quantitative expression level of lncRNAs between patients and healthy groups. Spearman’s rank correlation test was performed to evaluate the possible correlation between the relative expression levels of lncRNAs and the clinical data. P. Value Less than 0.5 is considered statistically significant.

Microarray data analysis

The analysis of microarray datas to investigate the expression changes of long non-protein-coding RNA in patients with MS was performed by R Studio software. Based on the analysis and investigations among the 35214 genes examined in GSE43591, 62 genes had a significant decrease in expression and 32 genes had a significant increase in expression. Figure 1 shows the top 20 genes with significant increase and decrease in expression in GSE43591. Among the studied genes, A2M-AS1 non-coding long RNA (logFC: -1.796157, adj.P. Val:0.01884) had significant expression reduction in MS samples compared to healthy ones (Figure2). This gene showed a biomarker potential to distinguish disease samples from healthy types, and Real-time PCR test to confirm their expression changes, determines the exact amount of biomarker potential of this gene.

According to the strong interactions and expression changes in MS, Lnc RNA A2M-AS1 considered as the target of this study. In next steps, the interactions of mRNAs with Lnc RNA A2M-AS1 were investigated in LncRRIsearch database and the highest interacted mRNAs were found (Table1). Then the interaction of miRNAs with targeted Lnc RNA were estimated in LncBase database and the strongest miRNAs were extracted (Table 2).

Genomic Distance	Type	Transcript Name	Transcript ID	Min of Energy	Sum of Energy	Num
10,000,000,000	mRNA	RPGR-009	ENST00000378505	-41.51	-2,329.61	1
10,000,000,000	mRNA	GOLGA6L22-002	ENST00000622895	-29.40	-1,553.99	2
10,000,000,000	mRNA	SNX8-001	ENST00000222990	-37.61	-1,383.99	3
10,000,000,000	mRNA	GOLGA6L6-001	ENST00000619213	-29.42	-1,348.21	4
10,000,000,000	mRNA	EMC10-001	ENST00000334976	-34.63	-1,258.59	5
10,000,000,000	mRNA	ABI2-001	ENST00000295851	-28.50	-1,039.04	6
10,000,000,000	mRNA	GOLGA6L1-001	ENST00000614055	-28.10	-933.24	7
10,000,000,000	mRNA	FAM98B-001	ENST00000397609	-44.10	-927.05	8
10,000,000,000	mRNA	GOLGA6L2-002	ENST00000567107	-28.46	-788.60	9
10,000,000,000	mRNA	CCDC85C-001	ENST00000380243	-35.25	-544.62	10

Table 1: Ten mRNAs that interact with Lnc RNA A2M-AS1. Chosen on Considering that this Lnc RNA has strong interactions and expression changes in MS.

Num	miRNA	MirBase ID
1	hsa-let-7a-5p	MIMAT0000062
2	hsa-miR-34a-5p	MIMAT0000255
3	hsa-miR-377-3p	MIMAT0000730
4	hsa-miR-7-5p	MIMAT0000252
5	hsa-miR-98-5p	MIMAT0000096
6	hsa-miR-106a-5p	MIMAT0000103
7	hsa-miR-181a-5p	MIMAT0000256
8	hsa-miR-19a-3p	MIMAT0000073
9	hsa-miR-210-3p	MIMAT0000267
10	hsa-miR-26a-5p	MIMAT0000082

Table 2: 10 miRNAs that interact with LncRNA A2M-AS1

For further investigations about Proteins and signaling pathways which are related with this LncRNA, Protein network interaction with RPGR mRNA as the strongest mRNA interacted with A2M-AS1 LncRNA was found in STRING database as it shown in Figure3. And the respective signaling pathways of these proteins were extracted from Enrichr database (Table 3).

Num	Term	P-value	Adjusted P-value	Odds Ratio
1	Cilium Assembly R-HSA-5617833	1.7176030321666058E-12	1.1336180012299598E-10	193.6731843575419
2	Organelle Biogenesis and Maintenance R-HSA-1852241	2.7104762766497688E-11	8.944571712944237E-10	128.77518656716418
3	RAS Processing R-HSA-9648002	0.010402987018789436	0.06655323472261897	110.95
4	Chaperone Mediated Autophagy R-HSA-9613829	0.010947779702855981	0.06655323472261897	105.10526315789474
5	MicroRNA (miRNA) Biogenesis R-HSA-203927	0.013124225781325726	0.06655323472261897	86.80869565217391
6	Cell Cycle, Mitotic R-HSA-69278	0.032108165359767765	0.08389858456518286	8.303689486031137
7	Selective Autophagy R-HSA-9663891	0.03305095755598113	0.08389858456518286	33.215
8	Signal Transduction R-HSA-162582	0.03717806377394846	0.08733841940403841	4.069890288500609
9	Gene Silencing by RNA R-HSA-211000	0.057835721217223385	0.09787583590607034	18.581308411214952
10	Autophagy R-HSA-9612973	0.06820614339416273	0.1088017183040322	15.639370078740157
11	MAPK1/MAPK3 Signaling R-HSA-5684996	0.14225898764381306	0.17387209600910486	7.142391304347826
12	Sensory Perception R-HSA-9709957	0.2912227105231511	0.33139136025048227	3.1502439024390245
13	Metabolism Of Lipids R-HSA-556833	0.33651972462527135	0.36410330861094936	2.634473324213406
14	Innate Immune System R-HSA-168249	0.44270309194825935	0.46378419156484313	1.8331721470019342
15	Gene Expression (Transcription) R-HSA-74160	0.5628625589346131	0.5804520139013197	1.2804558011049725
16	Immune System R-HSA-168256	0.675178955818414	0.6855663243694665	0.9292996910401647

Table3: cellular signaling pathways of mRNA RPGR coding protein gene and other protein networks mentioned in figure 3.

Gene ontology

Gene Ontology of proteins network interaction with mRNA RPGR, in this study was done in three fields including: Biological process, cellular component and molecular functions. Biological process including: Cilium Assembly, Plasma Membrane Bounded Cell Projection Assembly, Organelle Assembly, Renal System Development, Positive Regulation of Cell Junction Assembly, Positive Regulation of Intracellular Protein Transport, Positive Regulation of Nucleocytoplasmic Transport. Cellular Components are: Cilium, Sperm Flagellum, Cell-Cell Junction, Specific Granule Lumen, Microtubule Cytoskeleton. And molecular functions are: pre-miRNA Binding, Glycerophospholipid Flippase Activity, GTPase Inhibitor Activity, Kinesin Binding, GDP Binding.

Real-time PCR data analysis

The statistical analysis of the data obtained from the results obtained in the Q-PCR test and the design of the relevant graphs were done using Graph Pad Prism software (Version 9.0.0) and the examination of gene expression changes (DEGs) including -ΔΔCT.

Analysis of expression changes

Analysis of expression changes was done by Real-time PCR test to evaluate the changes predicted by microarray analysis. Based on the analysis, long non-coding RNA A2M-AS1 had a significant expression reduction in MS samples compared to healthy samples (logFC: -2.550, p-value: 0.0074) (Figure4). This result shows the accuracy of the results obtained through microarray analysis and strengthens the hypothesis of diagnostic biomarkers of A2M-AS1 gene. ROC analysis was performed to investigate the biomarker potential of this gene. Based on ROC biomarker analysis, A2M-AS1 Lnc RNA (AUC:0.7075, p-value:0.0248) considered as an acceptable potential diagnostic biomarker to distinguish MS patients from healthy samples (Figure 5). According to Mann-Whitney analysis to investigate the relationship between the expression level of LncRNA A2M-AS1 and patients ages (Figure6), and the patient's pathophysiological characteristics (Figure7) no significant relation was found.

Multiple Sclerosis (MS) is one of the most significant neurodegenerative diseases that endangers the lives of many people worldwide every year. The rate of MS disease was reported by WHO in 2022 to be about 2.8 million people worldwide and 100 cases out of every 100,000 people in Iran. The high and progressive rate of this disease, as one of the three common diseases and neurodegenerative indicators in the world, due to the lack of definitive treatment, requires extensive clinical research and more bioinformatic and pathological studies. Investigating the status of gene expression profiles plays a very important role in the recognition, management, control and prevention of various diseases, including neurodegenerative diseases. This study investigates the interaction and expression of non-coding gene in multiple sclerosis patients by using bioinformatics analyzes and laboratory research to find the gene expression pattern and the interaction of potential biomarkers of this disease and find appropriate treatment goals are of great importance. In this study, we investigated the mechanism of effect and interaction of long non-coding RNA A2M-AS1 in patients with multiple sclerosis.

According to the research of Dr. Yao and colleagues in 2021, it was shown that LncRNA A2M-AS1targets genes related to ferroptosis and plays a role in gastric cancer by increasing the expression of ferroptosis and activating CD4 + T cells(13). In recent studies and clinical research on this molecule, it has been shown that changes in the expression of the LncRNA A2M-AS1 gene in the occurrence of lung adenocarcinoma(14), the decrease in its expression in pancreatic cancer(15), the incidence of multiple myeloma and the development of cervical cancer are involved. Also, in Dr. Wang's studies in 2020, this gene was mentioned as a diagnostic biomarker of acute coronary syndrome, which plays an important role in this disease with its reduced expression(16). In Dr. Hong's studies in 2022, it was shown that this LncRNA is a positive regulatory factor with an increased expression pattern to promote breast cancer metastasis (17). Through the interaction with PCBP3(18), increasing the expression in the diagnosis of progression and prognosis of breast cancer (19), decreasing the expression level of A2M-AS1 in the lung tissue of patients with COVID-19 severe compared to control subjects(20). In the studies of Dr. Guo in 2022, it was shown that the overexpression of A2M-AS1 through the regulation of miR-587/bmp3 inhibits cell growth and invasion in lung adenocarcinoma(19). Dr. Fang In 2020, regarding the role of this RNA in breast cancer, it was reported that positive regulation and increased expression of A2M-AS1 promotes invasion and migration in this type of cancer and is a poor prognostic biomarker in breast cancer(19). In this field, Dr. Liu's research in 2020 also acknowledged that overexpression of A2M-AS1 LncRNA increases tumor growth by reducing the expression of miR-146b in breast cancer.(14) The comprehensive analysis of LncRNA-A2MAS1 regulatory networks in Alzheimer's disease in 2022 by Dr. Li showed that this RNA has a direct effect on the effective mechanisms in the occurrence of this disease, which includes autophagy and apoptosis(21). that by following similar signaling pathways, the possible and similar effect of this LncRNA in MS can also be addressed. Other mRNAs examined include: ZMAT3, CCDC113, CMPK1, MCC, KCNK2, KCTD15, PIM1, GJA1, TMED5, BMPR1B, WEE1, CYBRD1, MAPK1, CRKL, PHKA1, DICER1, SEMA7A, RRAGD, ABHD17C, TOB1, FOXP4, ADD3, CMTM4, TGFBR3, ARHGEF26, SEMA6D, PREX2, GABRB1, PDLIM5, HNRNPF, RHOQ, ANKFY1, USP46, MTSS1, UXS1, PPP2CB, NOTCH2, FGFRL1, ZNF711, ROCK1, GNG12, CTSA, SUN2, SMAD1, LONRF1 and from Among the key LncRNAs investigated in multiple sclerosis disease, we can mention C17orf82, BOLA3-AS1, RBM26-AS1, RBMS3-AS3, CRNDE, FAM13A-AS1, VENTXP1 according to the studies of Dr. Hao and colleagues in 2022(22). Also, in the studies of Dr. Panahi, it was shown that the allele of the TPH gene rs17110747 is significantly enriched in Iranian patients with multiple sclerosis, which is an important reason for the high incidence rate of this disease in Iran(23). According to the different pattern of gene expression profile of LncRNA A2M-AS1 in different diseases that were mentioned compared to its expression profile in MS, it can be concluded that the expression changes of it in different diseases has different roles, which play in signaling pathways can suppress or aggravate a disease. Since there are no clear signaling pathways and much information about the exact effect mechanism of genes in neurodegenerative diseases, especially MS, due to the involvement and participation of and A2M-AS1 LncRNA in the mTOR-PI3K AKT signaling pathway in other neurodegenerative diseases such as Alzheimer which was mentioned earlier, it can be inferred that it has a synergistic or regulatory effect in MS as well, it is suggested to investigate the mechanism of effect of this lncRNA on other more genes in MS disease, future researches should be carried out. In this research, for the first time in the world, an attempt was made to take an effective step in the early diagnosis and prognosis of this disease, with the aim of investigating the changes in the expression of gene in multiple sclerosis by relying on the mentioned biomarker.

Based on the tests and analyzes performed in this study, it was shown that A2M-AS1 LncRNA in the Microarray data in MS samples have a significant decrease in expression compared to control samples. Real-time PCR test also confirmed the results of bioinformatics analysis and showed that the expression of this gene in MS samples has a significant decrease in expression. These results make the hypothesis of diagnostic biomarkers of this gene stronger. Based on ROC statistical analysis performed on Real-time PCR data, it was shown that A2M-AS1 LncRNA identified an excellent diagnostic biomarker. This result can strengthen the hypothesis that the reduction of A2M-AS1 expression is probably one of the possible reasons for subsequently increasing the risk of MS disease.

Ethics approval and consent to participate: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Ethics Committee of Isfahan University of Medical Sciences.

Consent for publication: Informed consent was obtained from all individual participants included in the study.

Availability of data and materials: The datasets generated or analyzed during the current study are available in the GEO repository, GSE43591.

Competing interests: The authors declare that they have no competing interests.

Funding: Not applicable.

Authors' contributions: Shaghayegh Mohammadi performed the experiments, data analysis, and visualization as the first author. Mansoureh Azadehand Tahereh Saeghyian are equally contributed as second authors. Mansoureh Azadeh is the corresponding author and have revised the manuscript and validated the experiment. Mohammad Rezaei had been contributed in this study as data analyzer helper.

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No competing interests reported.

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Initial evaluation of LncRNA A2M-AS1 gene expression in multiple sclerosis patients

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Abstract

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Introduction

Method & materials

Gene expression analyzes

Sampling

RNA extraction

cDNA synthesis and Real-Time PCR

Statistical analysis

Result

Discussion

Conclusion

Declarations

References

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