BDNF-AS is a promising downregulated biomarker for the diagnosis in different disease types in multiple sclerosis

doi:10.21203/rs.3.rs-1956394/v1

Download PDF

Research Article

BDNF-AS is a promising downregulated biomarker for the diagnosis in different disease types in multiple sclerosis

https://doi.org/10.21203/rs.3.rs-1956394/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Multiple sclerosis (MS) is an autoimmune demyelinating disease of the central nervous system and the most common neurological disease in young adults, affecting more than 2.5 million people worldwide. The disease progression is likely to be the result of neurodegeneration. Numerous therapies that have emerged to treat relapsing forms of MS have been unsuccessful in slowing or halting progressive forms of the disease, during which inflammatory activity is replaced by axonal loss and atrophy. BDNF-AS is a type of long non-coding RNAs that has a negative transcriptional regulation over the neurotrophin BDNF, which is known to be highly expressed in actively demyelinating MS lesions and which has a function in neuron survival and plasticity necessary for neuroprotection during inflammation. BDNF has been shown to be low in older and chronic MS plaques, which could be a contributing factor to the ongoing degenerative changes in the chronic progressive stages of MS. However, few studies have investigated the role of BDNF-AS and how it may contribute to the role of BDNF in MS. Therefore, we studied the relative expression of BDNF-AS in the serum of patients with different disease types of MS via qRT-PCR. We found that BDNF-AS is significantly downregulated in MS as compared to controls. We also determined a relative expression cut-off of (0.31) fold change, which can be used for the diagnosis of MS with high specificity and sensitivity.

Multiple Sclerosis

BDNF

BDNF-AS

RRMS

SPMS

PPMS

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system (CNS), characterized by axonal degeneration and gliosis (X. Yang et al. 2018), and is the most prevalent chronic, autoimmune-mediated inflammatory disease of the central nervous system (Lodde et al. 2020). (MS) is considered to be the most common neurological disease in young adults, affecting more than 2.5 million people worldwide (Attfield et al. 2022). It is described by continuous inflammatory activity within the CNS, giving rise to tissue damage that results in brain and spinal cord atrophy and irreversible neurological disability (Machcińska et al. 2022).

The majority ( ~ 85- 90%) of patients with MS initially have a relapsing–remitting disease (RRMS) course, (~90% ) of patients with RRMS exhibit a secondary progressive disease (SPMS) and (10%) of MS patients experience primary-progressive MS (PPMS) (Dutta and Trapp 2007). It has long been recognized that although patients may essentially share the diagnostic label of MS, there is considerable variation across the disease course in terms of clinical phenotype, prognosis, MRI appearances, immunological characteristics and pathology and one of the most intriguing enigmas of MS is why some patients have a rather disease benign course, whereas others may experience an aggressive disease course (Wilkins 2018). While much has been learned about the mechanisms of immune dysregulation underlying relapsing forms of MS, less is known about progressive MS pathophysiology (Miller 2017). Although disease activity in MS is predominantly inflammatory, disease progression is likely to be the result of neurodegeneration (Harris and Sadiq 2009). Likewise, the numerous therapies that have emerged to treat relapsing forms of MS—all targeting some aspect of the immune system—have largely been unsuccessful in slowing or halting progressive forms of the disease, during which inflammatory activity is replaced by axonal loss and atrophy (Miller 2017).

The neurotrophin known as BDNF (Brain-Derived Neurotrophic Factor) is one of the most important regulators of neuronal plasticity (Ksiazek-Winiarek, Szpakowski, and Glabinski 2015). BDNF belongs to the neurotrophin family, which also includes NGF (nerve growth factor) and NT3 and NT4 (neurotrophins 3 and 4) (Karimi et al. 2022). By increasing gene expression and protein synthesis, BDNF promotes neuron survival and cytoskeleton rearrangement necessary for the formation of new neuronal synapses. Brain-localized inflammation may increase BDNF concentrations, providing neuroprotection (Ksiazek-Winiarek, Szpakowski, and Glabinski 2015). Immune cells express BDNF in actively demyelinating MS lesions but not in those with no ongoing myelin breakdown (Nociti 2020). Studies have shown that BDNF levels in RRMS patients is higher than progressive MS patients, implying that MS progression may be due to a deficit in neuroprotection and repair mechanisms under chronic injury (Nociti 2020; Naegelin et al. 2020).

LncRNAs (Long non-coding RNAs) are transcripts of more than 200 nucleotides without a functional protein-coding ability (Xi et al. 2018). Many LncRNAs are expressed at low levels and have fairly high tissue or cell-type specificity (M. Yang et al. 2019). They have also currently emerged as important regulators in normal biological processes and disease pathogenesis (Kiang and Leung 2017). Similar to protein‑coding genes, LncRNAs vary considerably in function (Malik and Feng 2016). LncRNAs have been demonstrated to affect several important physiological processes by interacting with DNA, chromatin, signaling, regulatory proteins and a variety of cellular RNA species (Zhan, Li, and Wei 2018) . BDNF-AS (Brain-Derived Neurotrophic Factor-Anti-Sense), one type of LncRNAs, is found primarily on the antisense strand of the BDNF gene on (chromosome 11p14.1). BDNF-AS has been discovered to repress BDNF sense transcript expression by altering chromatin architecture at the BDNF locus, resulting in a decrease in endogenous BDNF protein expression and function (Ghafouri-Fard et al. 2021). The downregulation of BDNF-AS which has been found to inversely upregulate BDNF, results in a pro-neuronal effect in stimulating neuronal cellular growth and differentiation, including both in vitro and in vivo experiments (Zheng et al. 2016).

BDNF-AS expression, to our knowledge, was not thoroughly investigated in neurological diseases within patients’ blood and only analyzed in cell models and patients’ autopsies. Moreover, the only study on BDNF-AS in multiple sclerosis was on whole blood of RRMS patients where it finds no significant difference with the controls (Gharzi et al. 2018). Therefore, our study aims to investigate BDNF-AS expression in different disease types of MS and trying to correlate the expression with any of the disease prognostic parameters to find out how it may contribute to the role of BDNF in MS and reveal its diagnostic and prognostic ability in multiple sclerosis.

4.1 Study design and Population

This is a pilot case-control research that was performed on (54 subjects) diagnosed with multiple sclerosis at different stages and 20 age- and sex-matched healthy controls. All subjects were recruited during the period from (January 2021) to (May 2021) from Multiple Sclerosis Research Unit, Kasr Al-Ainy, Neurology department, Cairo University hospitals, Cairo, Egypt. Written informed consent was obtained from all patients and controls. The study was approved by the ethical committee board of neurology department, Kasr Al-Ainy hospital, Cairo University.

4.2 Subjects

All subjects were diagnosed according to the International Panel on Diagnosis of Multiple Sclerosis “McDonald's criteria” 2010 (Polman et al. 2011). The study consisted of total (74 subjects), divided into (31 RRMS) group, (13 SPMS) group, (10 PPMS) group and (20 healthy control) group. Both RRMS group and SPMS group were subdivided into (16 Relapse RRMS), (15 Remitting RRMS), (8 Relapse SPMS) and (5 Remitting SPMS). The clinical disability was assessed using the Kurtzke Expanded Disability Status Scale (EDSS). Relapse was defined by the appearance of new neurological symptoms or worsening of pre-existing neurological symptoms lasting at least 24 hours in a patient who had been neurologically stable or improving for the previous 30 days accompanied by objective changes on neurological examination (worsening on the expanded disability status scale “EDSS” of 0.5 point or a worsening by 1.0 point on the pyramidal, cerebellar, brainstem or visual functional system scores) (Kurtzke 1983).

4.3 Exclusion criteria

Subjects were excluded from the study if they have a history of other associated autoimmune disease, malignancy, liver disease, diabetes mellitus, hypertension, cerebrovascular disease, pregnant females, or any inflammatory or infectious diseases in the previous month. Smoking or alcoholic subjects were also considered non-eligible for the study.

4.4 Demographics and disease characteristics in MS subjects

The following clinical data were collected from all participants in this study:

Age & Sex. (Statistical analysis was carried out on the data to ensure non-significant difference i.e., Age- and sex-matched groups)
Clinical picture of the patient regarding any existing or past disease.
Family History of MS or any other autoimmune disease.
History and duration of MS.
Status of the current examination (Relapse/Remitting) at the time of sampling.
Number of relapses within the last 3 years.
EDSS

4.5 Specimen collection

Five milliliters of whole blood were drawn from patients and controls. Each blood sample was left at 37 °C for 30 minutes before being centrifuged at 3000 rpm for 10 minutes. Sera were divided into two aliquots and frozen at -80 °C until use.

4.6 RNA extraction

Total RNA was extracted from 200 µl of pre-isolated serum using miRNeasy Serum/Plasma Kit (QIAGEN, Catalog no. 217184) according to the manufacturer protocol. An aliquot of ~ 14 µl was eluted and analyzed for concentration and purity using (NanoDrop One spectrophotometer^TM, ThermoFisher) and then stored at −80 °C till use.

4.7 Complementary DNA (cDNA) synthesis

Complementary DNA (cDNA) was synthesized using 2.5 mg total RNA using RT² First Strand Kit (QIAGEN, Catalog no.330404) according to the manufacturer's protocol. Reverse transcription was performed at 42°C for 5 minutes, 37°C for 60 minutes, and 95°C for 5 minutes, and the cDNA obtained was stored at - 20°C till use. The reverse transcription was carried out using (SimpliAmp^TM Thermal Cycler-Applied Biosystems).

4.8 Real-time PCR

Using cDNA obtained from the previous steps, relative gene expression for (BDNF-AS) and a reference gene (GAPDH) were quantified using the following primer kits: RT² lncRNA qPCR Assay (QIAGEN, Catalog no.330701) with the following IDs: (BDNF-AS) (ID: LPH15814A-200) and (GAPDH) (ID: LPH15126A-200). All of the PCR reaction were performed using the Mastermix kit: RT² SYBR Green ROX qPCR Mastermix (QIAGEN, Catalog no.330520). The Real-time PCR conditions were 95°C for 10 minutes and 40X (95°C for 15 seconds, 60°C for 1 minute) followed by a melt curve analysis. All Real-time PCR reactions and the melt curve analysis were carried out using (QuantStudio^TM 1 Real-Time PCR- Applied Biosystems).

4.9 Relative Gene Expression

The relative expression of (BDNF-AS) was calculated for each sample by analyzing the results of RT-PCR using double-delta Ct analysis. The Ct values for all samples were normalized against (GAPDH). Fold Change (FC) was calculated for each sample then compared between patients and normal controls for the determination if (BDNF-AS) is relatively differentiated in MS.

4.10 Statistical analysis

SPSS version 26 (Chicago, IL, USA) was used for statistical analyses. Normality testing was done on all data to determine parametric from non-parametric data. For the analysis of difference between groups, One-way ANOVA, or the related non-parametric test, Kruskal-Wallis test, was used depending on the type of data being analyzed (Parametric Vs. Non-Parametric). To investigate the source of any differences between the groups, multiple comparisons tests were performed. Similarly, Pearson or Spearman test of correlation was used for parametric or non-parametric data, respectively. ROC curve analysis was also used to draw a conclusion about the usefulness of the diagnostic ability of (BDNF-AS). P-value of < 0.05 was considered statistically significant. All figures and tables

5.1 Demographics and disease characteristics in MS subjects

When comparing the age and sex across the disease groups and the control group, all the groups showed no difference regarding age and sex, i.e., all the groups are age and sex matched. Regarding family history of MS, there were only two cases within RRMS group representing (6.5%) of cases and only one case within SPMS group that represents (7.7%) of cases within the group. Relapse/Remitting grouping were established on both RRMS and SPMS groups and found to have 16 Relapse RRMS, 8 Relapse SPMS, 15 Remitting RRMS and 5 Remitting SPMS. The number of relapses within the last three years (Median ± IQR) were found to be (2 ± 2) in RRMS and (4 ± 2) in SPMS. EDSS (Mean ± SD) were (3.05 ± 2.74) for RRMS, (6.00 ± 1.02) for SPMS and (5.55 ± 1.14) for PPMS. Demographic and clinical characteristics of all included subjects and controls are summarized in Table 1.

Table 1 Demographics and disease characteristics in MS subjects and controls

	Age ^a	Gender ^c		Family History of MS ^c		Status ^c		Duration of MS ^a	No. of Relapses ^{b, d}	EDSS^a
	Age ^a	Male	Female	Yes	No	Relapse	Remitting	Duration of MS ^a	No. of Relapses ^{b, d}	EDSS^a
RRMS (n=31)	29.06 ± 9.08	13 (42%)	18 (58%)	2 (6.5 %)	29 (93.6 %)	16 (51.6%)	15 (48.4%)	5.11 ± 4.65	2 ± 2	3.05 ± 2.74
SPMS (n=13)	35.02 ± 16.05	6 (46%)	7 (54%)	1 (7.7%)	12 (92.3%)	8 (61.5%)	5 (38.5%)	15.12 ± 8.66	4 ± 2	6.00 ± 1.02
PPMS (n=10)	32.09 ± 6.01	4 (40 %)	6 (60 %)	0 (0%)	10 (100%)	-	-	10.8 ± 9.9	-	5.55 ± 1.14
Control (n=20)	37.03 ± 13.02	9 (45%)	11 (55%)	0 (0%)	20 (100%)	-	-	-	-	-
P-value	0.382	0.883

^a (Mean ± SD), ^b (Median ± IQR), ^c (%Frequency), ^d (Within the last 3 years)

5.2 Relative expression of (BDNF-AS)

(BDNF-AS) was found to be down-expressed with high significance (P-value < 0.01) in multiple sclerosis as compared to the control subjects. The fold change as compared to the control (Mean ± SE) was (0.19 ± 0.06) for PPMS, (0.33 ± 0.07) for RRMS relapse, (0.30 ± 0.08) for RRMS Remitting, (0.33 ± 0.11) for SPMS Relapse, (0.83 ± 0.20) for SPMS Remitting. The data can be summarized within Table 2

Table 2 Relative expression of (BDNF-AS)

Subjects

Control

PPMS

RRMS

SPMS

P-value

Kruskal-Wallis’s statistic

Relapse

Remitting

Relapse

Remitting

Fold Change

± SE ^a

1.12

± 0.052

0.19

± 0.06

0.33

± 0.07

0.30

± 0.08

0.33

± 0.11

0.83

± 0.20

> 0.01 ^**

28.919

^a Standard error, ** highly significant.

Multiple comparison tests found all the groups to be significantly different from the control, except for the (SPMS-Remitting) group, which was not significantly different. However, all the groups showed no difference from each other. This can be visualized in Figure 1

5.3 ROC curve analysis

Based on the above findings, ROC curve was created, and AUC was calculated to evaluate the diagnostic values of (BDNF-AS). The calculated value of (AUC ± S.E) was (0.869 ± 0.041) with a 95% confidence interval (0.789–0.948). According to the highest Youden index value for all the possible cut-offs, the best cut-off was (0.31) which represents (75.93%) sensitivity and (100%) specificity. These findings are summarized in Figure 2 and Table 3.

Table 3 ROC curve analysis of (BDNF-AS)

LncRNAs	AUC	SE	95% CI	P-value	Youden index	Cut-off	Sensitivity	Specificity
BDNF-AS	0.869	0.041	0.789–0.948	> 0.001	0.759	0.31	75.93 %	100 %

AUC=area under the receiver operating characteristic curve, SE=standard error, CI=confidence interval, P-value, compared with AUC of 0.5

5.4 Correlation analysis:

Spearman non-parametric correlation was carried out to investigate the relationship of some demographic and disease characteristics (Age, duration of MS, number of relapses within the last 3 years and EDSS) with BDNF-AS expression that was measured for each patient. The results showed no significant correlation with any of them. The values can be summarized in Table 4

Table 4 Correlation analysis of some demographic characteristics with BDNF-AS expression

	Age		Duration of MS		No. of Relapses ^a		EDSS
	r	P-value	r	P-value	r	P-value	r	P-value
BDNF-AS	0.194	0.208	-0.022	0.885	0.130	0.399	0.214	0.162

^aWithin the last 3 years, r=correlation coefficient, EDSS=Expanded Disability Status Scale

BDNF, a protein-coding gene, and BDNF-AS, a LncRNA gene, are transcribed from chromosome 11. Being the antisense of BDNF, the LncRNA BDNF-AS exert transcriptional regulation for BDNF via altering the chromatin architecture (Ghafouri-Fard et al. 2021). A report by Modarresi et al., discovered that inhibiting BDNF-AS can increase endogenous BDNF, stimulate neurite formation, and cause neuronal differentiation (Modarresi et al. 2012).

BDNF in MS has some interesting findings. It was found that immune cells secrete BDNF in MS lesions but only in those with active myelin degeneration (Nociti 2020). Despite this finding, it was found that the concentration of BDNF in the cerebrospinal fluid (CSF) was very low (Naegelin et al. 2020). Not only that but it was also found that, when compared to healthy controls, the levels of BDNF in the serum, CSF, and PBMC (Peripheral Blood Mononuclear cells), are lower in MS patients (Hamamcioglu and Reder 2007; Azoulay et al. 2005). Endogenous neurotrophins, however, are low in older and chronic MS plaques. This could be a contributing factor to the ongoing degenerative changes in the chronic progressive stage of MS (Nociti 2020). Studies have shown that BDNF protein concentration is decreased in the serum of MS patients compared with controls (Islas-Hernandez et al. 2018; Tongiorgi et al. 2012; Naegelin et al. 2020; Azoulay et al. 2005; Hamamcioglu and Reder 2007). Interestingly, the level of BDNF in SPMS were lower than both RRMS and PPMS (Naegelin et al. 2020). BDNF secretion by immune cells is higher in RRMS patients compared to progressive MS patients, implying that MS progression may be due to a deficit in neuroprotection and repair mechanisms under chronic injury (Nociti 2020). Other neurological diseases showed similar pattern with low levels of BDNF in the serum such as schizophrenia (Ikeda et al. 2008), Depression (Molendijk et al. 2014), Alzheimer’s (Laske et al. 2007; Lee et al. 2009) and Huntington’s disease (Zuccato et al. 2011; Ciammola et al. 2007). Autism, However, showed higher levels of BDNF in the serum (Armeanu, Mokkonen, and Crespi 2017).

However, the role of BDNF-AS does not always depend on the suppression of BDNF as it was found that in neoplastic conditions that its role mainly depends on BDNF-independent pathways (Ghafouri-Fard et al. 2021). Several studies have found that BDNF-AS shares functional interactions, both direct and indirect, with GSK‐3β (glycogen synthase kinase-3β) , EZH2 (Enhancer of zeste homolog 2) in colorectal cancer (Zhi and Lian 2019), miR-214 in Esophageal cancer (Zhao et al. 2018), PABPC1 (polyadenylate-binding protein cytoplasmic) (Su et al. 2020) and p53 (Lv, Gu, and Guo 2020), in Glioblastoma.

In our study, we decided to investigate the levels of BDNF-AS in serum of MS patients in all types of MS and compare them with levels of BDNF-AS in healthy controls in order to reveal the relation it has with pre-published results of both BDNF and BDNF-AS and also to evaluate its diagnostic and prognostic ability in MS. Interestingly, we found that the levels of BDNF-AS are decreased significantly in serum of patients with MS when compared to healthy controls. This could be the result of a downregulating mechanism inhibiting BDNF-AS in order to upregulate BDNF in MS lesions to stimulate the synaptic plasticity and myelin regeneration in MS plaques. One interesting finding, that could be contradicting our result, is that BDNF itself is being downexpressed in serum of MS patients (Islas-Hernandez et al. 2018; Tongiorgi et al. 2012; Naegelin et al. 2020; Azoulay et al. 2005; Hamamcioglu and Reder 2007). This could contradict the theory of BDNF-AS being downregulated in order to permit overexpression of BDNF in MS. However, the fact about the levels of BDNF in the serum or CSF, which are decreased in MS patients (Hamamcioglu and Reder 2007; Azoulay et al. 2005), may suggest that BDNF is only exclusively available in MS lesions and not being secreted in body fluids. This finding is in line with a study conducted by Gharzi et al., on patients with RRMS, that found that the correlation between (BDNF) and (BDNF-AS) may not always be a negative correlation (Gharzi et al. 2018). This fact may also extend to other diseases such as (bipolar disorder), (gastric cancer) and (breast cancer) in which the authors of those studies found that positive correlation between (BDNF) and (BDNF-AS) may also exist despite what is already known about the negative regulation of BDNF by the action of BDNF-AS (Ghafouri-Fard et al. 2021; Gharzi et al. 2018)

In our study, SPMS showed the highest BDNF-AS expression among MS subtypes and was the only group that represented non-significant downregulation compared to controls. This may be in line with results that showed (BDNF), the neurotrophic factor under negative transcriptional regulation of (BDND-AS), is highly produced in RRMS while showing lower expression in progressive MS (Nociti 2020; Tongiorgi et al. 2012).

Moreover, we found that BDNF-AS expression may be used for the diagnosis of MS with high specificity and sensitivity with a relative expression cut-off of (0.31) fold change that represents the best balance between sensitivity and specificity. Our study, however, failed to find any significant difference between RRMS, PPMS or SPMS groups which may be necessary for the diagnosis of different MS types. Furthermore, BDNF-AS did not show any significant correlation with age, duration of MS, number of relapses or EDSS. However, this may be the result of a low sample size which are not enough for any firm conclusions to be undertaken.

BDNF-AS expression is a promising biomarker for the diagnosis of MS showing downregulation in serum of MS patients with high specificity and sensitivity. However, despite this finding, more research is required on larger samples in each type of MS. Finally, we recommend doing expression analysis of BDNF-AS in both cell cultures and autopsies of brains and spinal cords from MS patients in order to find the relation of BDNF-AS with BDNF in lesions within CNS to gain more insights on the disease regulation and future treatments.

Acknowledgement

The authors expressed their gratitude to the staff members of Multiple Sclerosis Research Unit, Kasr Al-Ainy, Neurology department, Cairo University hospitals, Cairo, Egypt, for their support in the selection of MS cases under study. The authors also would like to thank prof./ Sayed Abdel Azeem Alsharkawy, Prof./ Sherif Fahmy and prof./ Sahar El-Saka for their sincere help and facilitations during the practical work in the faculty of Science, Suez university, Egypt.

Conflict of interest

Authors affirmed that there aren't any financial conflicts of interest to divulge

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Armeanu, Raluca, Mikael Mokkonen, and Bernard Crespi. 2017. “Meta-Analysis of BDNF Levels in Autism.” Cellular and Molecular Neurobiology 37 (5): 949–54.
Attfield, Kathrine E, Lise Torp Jensen, Max Kaufmann, Manuel A Friese, and Lars Fugger. 2022. “The Immunology of Multiple Sclerosis.” Nature Reviews Immunology, 1–17.
Azoulay, David, Veronika Vachapova, Boris Shihman, Ariel Miler, and Arnon Karni. 2005. “Lower Brain-Derived Neurotrophic Factor in Serum of Relapsing Remitting MS: Reversal by Glatiramer Acetate.” Journal of Neuroimmunology 167 (1–2): 215–18.
Ciammola, A, J Sassone, M Cannella, S Calza, B Poletti, L Frati, F Squitieri, and V Silani. 2007. “Low Brain‐derived Neurotrophic Factor (BDNF) Levels in Serum of Huntington’s Disease Patients.” American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 144 (4): 574–77.
Dutta, Ranjan, and Bruce D Trapp. 2007. “Pathogenesis of Axonal and Neuronal Damage in Multiple Sclerosis.” Neurology 68 (22 suppl 3): S22–31.
Ghafouri-Fard, Soudeh, Tayyebeh Khoshbakht, Mohammad Taheri, and Mahsa Ghanbari. 2021. “A Concise Review on the Role of BDNF-AS in Human Disorders.” Biomedicine & Pharmacotherapy 142: 112051. https://doi.org/https://doi.org/10.1016/j.biopha.2021.112051.
Gharzi, V, M Ganji, A Sayad, M Mazdeh, S Arsang-Jang, and M Taheri. 2018. “Expression Analysis of BDNF Gene and BDNF-As Long Noncoding RNA in Whole Blood Samples of Multiple Sclerosis Patients: Not Always a Negative Correlation between Them.” Iranian Journal of Allergy, Asthma and Immunology 17 (6): 548–56. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059556306&partnerID=40&md5=34a00b228a9724f27b0befe2dda9cb9c.
Hamamcioglu, K, and A T Reder. 2007. “Interferon-β Regulates Cytokines and BDNF: Greater Effect in Relapsing than in Progressive Multiple Sclerosis.” Multiple Sclerosis Journal 13 (4): 459–70.
Harris, Violaine K, and Saud A Sadiq. 2009. “Disease Biomarkers in Multiple Sclerosis.” Molecular Diagnosis & Therapy 13 (4): 225–44. https://doi.org/10.1007/BF03256329.
Ikeda, Yumiko, Noriaki Yahata, Itsuo Ito, Masatoshi Nagano, Tomoko Toyota, Takeo Yoshikawa, Yoshiro Okubo, and Hidenori Suzuki. 2008. “Low Serum Levels of Brain-Derived Neurotrophic Factor and Epidermal Growth Factor in Patients with Chronic Schizophrenia.” Schizophrenia Research 101 (1–3): 58–66.
Islas-Hernandez, Azul, Hugo Seacatl Aguilar-Talamantes, Brenda Bertado-Cortes, Georgina de Jesus Mejia-delCastillo, Raul Carrera-Pineda, Carlos Fredy Cuevas-Garcia, and Paola Garcia-delaTorre. 2018. “BDNF and Tau as Biomarkers of Severity in Multiple Sclerosis.” Biomarkers in Medicine 12 (7): 717–26.
Karimi, Nastaran, Helia Ashourizadeh, Boshra Akbarzadeh Pasha, Maryam Haghshomar, Tahmineh Jouzdani, Parnian Shobeiri, Antônio L Teixeira, and Nima Rezaei. 2022. “Blood Levels of Brain-Derived Neurotrophic Factor (BDNF) in People with Multiple Sclerosis (MS): A Systematic Review and Meta-Analysis.” Multiple Sclerosis and Related Disorders 65: 103984. https://doi.org/https://doi.org/10.1016/j.msard.2022.103984.
Kiang, Karrie M Y, and Gilberto K K Leung. 2017. “Clinical Significance of CRNDE Transcript Variants in Glioblastoma Multiforme.” Non-Coding RNA Research 2 (2): 119–21.
Ksiazek-Winiarek, Dominika Justyna, Piotr Szpakowski, and Andrzej Glabinski. 2015. “Neural Plasticity in Multiple Sclerosis: The Functional and Molecular Background.” Neural Plasticity.
Kurtzke, John F. 1983. “Rating Neurologic Impairment in Multiple Sclerosis: An Expanded Disability Status Scale (EDSS).” Neurology 33 (11): 1444.
Laske, Christoph, Elke Stransky, Thomas Leyhe, Gerhard W Eschweiler, Walter Maetzler, Andreas Wittorf, Surjo Soekadar, Elke Richartz, Niklas Koehler, and Mathias Bartels. 2007. “BDNF Serum and CSF Concentrations in Alzheimer’s Disease, Normal Pressure Hydrocephalus and Healthy Controls.” Journal of Psychiatric Research 41 (5): 387–94.
Lee, Jung Goo, Bae Sub Shin, Young Sun You, Ji Eun Kim, Sung Wook Yoon, Dong Wook Jeon, Jun Hyung Baek, Sung Woo Park, and Young Hoon Kim. 2009. “Decreased Serum Brain-Derived Neurotrophic Factor Levels in Elderly Korean with Dementia.” Psychiatry Investigation 6 (4): 299.
Lodde, Valeria, Giampaolo Murgia, Elena Rita Simula, Maristella Steri, Matteo Floris, and Maria Laura Idda. 2020. “Long Noncoding RNAs and Circular RNAs in Autoimmune Diseases.” Biomolecules 10 (7): 1044.
Lv, Xinwen, Chunyan Gu, and Shiwen Guo. 2020. “Activation of BDNF-AS/ADAR/p53 Positive Feedback Loop Inhibits Glioblastoma Cell Proliferation.” Neurochemical Research 45 (2): 508–18.
Machcińska, Maja, Magdalena Kierasińska, Martyna Michniowska, Marta Maruszewska-Cheruiyot, Ludmiła Szewczak, Rafał Rola, Anna Karlińska, Michael Stear, and Katarzyna Donskow-Łysoniewska. 2022. “Reduced Expression of PD-1 in Circulating CD4+ and CD8+ Tregs Is an Early Feature of RRMS.” International Journal of Molecular Sciences 23 (6): 3185.
Malik, Bhavna, and Felix Y Feng. 2016. “Long Noncoding RNAs in Prostate Cancer: Overview and Clinical Implications.” Asian Journal of Andrology 18 (4): 568.
Miller, Aaron. 2017. Handbook of Relapsing- Remitting Multiple Sclerosis. https://doi.org/10.1007/978-3-319-40628-2.
Modarresi, Farzaneh, Mohammad Ali Faghihi, Miguel A Lopez-Toledano, Roya Pedram Fatemi, Marco Magistri, Shaun P Brothers, Marcel P Van Der Brug, and Claes Wahlestedt. 2012. “Inhibition of Natural Antisense Transcripts in Vivo Results in Gene-Specific Transcriptional Upregulation.” Nature Biotechnology 30 (5): 453–59.
Molendijk, M L, P Spinhoven, M Polak, B A A Bus, BWJH Penninx, and B M Elzinga. 2014. “Serum BDNF Concentrations as Peripheral Manifestations of Depression: Evidence from a Systematic Review and Meta-Analyses on 179 Associations (N= 9484).” Molecular Psychiatry 19 (7): 791–800.
Naegelin, Yvonne, Katharina Saeuberli, Sabine Schaedelin, Hayley Dingsdale, Stefano Magon, Sergio Baranzini, Michael Amann, et al. 2020. “Levels of Brain-Derived Neurotrophic Factor in Patients with Multiple Sclerosis.” Annals of Clinical and Translational Neurology 7 (11): 2251–61. https://doi.org/https://doi.org/10.1002/acn3.51215.
Nociti, Viviana. 2020. “What Is the Role of Brain Derived Neurotrophic Factor in Multiple Sclerosis Neuroinflammation?” Neuroimmunology and Neuroinflammation 7 (3): 291–99. https://doi.org/10.20517/2347-8659.2020.25.
Polman, Chris H, Stephen C Reingold, Brenda Banwell, Michel Clanet, Jeffrey A Cohen, Massimo Filippi, Kazuo Fujihara, Eva Havrdova, Michael Hutchinson, and Ludwig Kappos. 2011. “Diagnostic Criteria for Multiple Sclerosis: 2010 Revisions to the McDonald Criteria.” Annals of Neurology 69 (2): 292–302.
Su, Rui, Jun Ma, Jian Zheng, Xiaobai Liu, Yunhui Liu, Xuelei Ruan, Shuyuan Shen, Chunqing Yang, Di Wang, and Heng Cai. 2020. “PABPC1-Induced Stabilization of BDNF-AS Inhibits Malignant Progression of Glioblastoma Cells through STAU1-Mediated Decay.” Cell Death & Disease 11 (2): 1–17.
Tongiorgi, Enrico, Arianna Sartori, Gabriele Baj, Alessio Bratina, Francesco Di Cola, Marino Zorzon, and Gilberto Pizzolato. 2012. “Altered Serum Content of Brain-Derived Neurotrophic Factor Isoforms in Multiple Sclerosis.” Journal of the Neurological Sciences 320 (1–2): 161–65.
Wilkins, Alastair. 2018. Progressive Multiple Sclerosis. Second Edi. https://doi.org/10.1007/978-3-319-65921-3.
Xi, Jiajie, Qiaoyi Sun, Li Ma, and Jiuhong Kang. 2018. “Long Non-Coding RNAs in Glioma Progression.” Cancer Letters 419: 203–9.
Yang, Meiqin, Zhensheng Zhai, Shuang Guo, Xiaoxi Li, Yongxia Zhu, and Yue Wang. 2019. “Long Non-Coding RNA FLJ33360 Participates in Ovarian Cancer Progression by Sponging miR-30b-3p.” OncoTargets and Therapy 12: 4469.
Yang, Xuan, Yuzhang Wu, Bei Zhang, and Bing Ni. 2018. “Noncoding RNAs in Multiple Sclerosis.” Clinical Epigenetics 10 (1): 1–12.
Zhan, Lei, Jun Li, and Bing Wei. 2018. “Long Non-Coding RNAs in Ovarian Cancer.” Journal of Experimental & Clinical Cancer Research 37 (1): 120.
Zhao, H, C Diao, X Wang, Y Xie, Y Liu, X Gao, J Han, and S Li. 2018. “LncRNA BDNF-AS Inhibits Proliferation, Migration, Invasion and EMT in Oesophageal Cancer Cells by Targeting miR-214.” Journal of Cellular and Molecular Medicine 22 (8): 3729–39. https://doi.org/10.1111/jcmm.13558.
Zheng, Xiaozhu, Chunshui Lin, Yuhong Li, Jing Ye, Jiali Zhou, and Peipei Guo. 2016. “Long Noncoding RNA BDNF-AS Regulates Ketamine-Induced Neurotoxicity in Neural Stem Cell Derived Neurons.” Biomedicine & Pharmacotherapy 82: 722–28. https://doi.org/https://doi.org/10.1016/j.biopha.2016.05.050.
Zhi, Hui, and Jiayu Lian. 2019. “LncRNA BDNF-AS Suppresses Colorectal Cancer Cell Proliferation and Migration by Epigenetically Repressing GSK-3β Expression.” Cell Biochemistry and Function 37 (5): 340–47. https://doi.org/https://doi.org/10.1002/cbf.3403.
Zuccato, Chiara, Manuela Marullo, Barbara Vitali, Alessia Tarditi, Caterina Mariotti, Marta Valenza, Nayana Lahiri, Edward J Wild, Jenny Sassone, and Andrea Ciammola. 2011. “Brain-Derived Neurotrophic Factor in Patients with Huntington’s Disease.” PLoS One 6 (8): e22966.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

BDNF-AS is a promising downregulated biomarker for the diagnosis in different disease types in multiple sclerosis

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1