- Email: [email protected]
MicroRNA expression profiles of multiple system atrophy from formalin-fixed paraffin-embedded samples
Accepted Manuscript Title: MicroRNA expression profiles of multiple system atrophy from formalin-fixed paraffin-embedded samples Author: Koichi Wakabayashi Fumiaki Mori Akiyoshi Kakita Hitoshi Takahashi Shinya Tanaka Jun Utsumi Hidenao Sasaki PII: DOI: Reference:
S0304-3940(16)30786-8 http://dx.doi.org/doi:10.1016/j.neulet.2016.10.034 NSL 32370
To appear in:
Neuroscience Letters
Received date: Revised date: Accepted date:
29-8-2016 6-10-2016 19-10-2016
Please cite this article as: Koichi Wakabayashi, Fumiaki Mori, Akiyoshi Kakita, Hitoshi Takahashi, Shinya Tanaka, Jun Utsumi, Hidenao Sasaki, MicroRNA expression profiles of multiple system atrophy from formalin-fixed paraffin-embedded samples, Neuroscience Letters http://dx.doi.org/10.1016/j.neulet.2016.10.034 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Research article
MicroRNA expression profiles of multiple system atrophy from formalin-fixed paraffin-embedded samples
Koichi Wakabayashia, *, Fumiaki Moria, Akiyoshi Kakitab, Hitoshi Takahashic, Shinya Tanakad, Jun Utsumie, Hidenao Sasakie
a
Department of Neuropathology, Institute of Brain Science, Hirosaki University
Graduate School of Medicine, Hirosaki 036-8562, Japan b
Department of Pathological Neuroscience, Center for Bioresource-based Researches,
Brain Research Institute, University of Niigata, Niigata 951-8585, Japan c
Department of Pathology, Brain Research Institute, University of Niigata, Niigata 951-
8585, Japan d
Department of Cancer Pathology, Hokkaido University Graduate School of Medicine,
Sapporo 060-8638, Japan e
Department of Neurology, Hokkaido University Graduate School of Medicine, Sapporo
060-8638, Japan
*
Corresponding author.
E-mail address: [email protected] (K. Wakabayashi).
HIGHLIGHT
MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression.
Multiple system atrophy (MSA) is an adult-onset neurodegenerative disorder.
We evaluated 50 formalin-fixed paraffin-embedded (FFPE) samples from patients with MSA.
Archived FFPE postmortem samples can be a valuable source for miRNA profiling in MSA.
ABSTRACT MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression. Recently, we have shown that informative miRNA data can be derived from archived formalin-fixed paraffin-embedded (FFPE) samples from postmortem cases of amyotrophic lateral sclerosis and normal controls. miRNA analysis has now been performed on FFPE samples from affected brain regions in patients with multiple system atrophy (MSA) and the same areas in neurologically normal controls. We evaluated 50 samples from patients with MSA (n = 13) and controls (n = 13). Twentysix samples were selected for miRNA analysis on the basis of the criteria reported previously: (i) a formalin fixation time of less than 4 weeks, (ii) a total RNA yield per sample of more than 500 ng, and (iii) sufficient quality of the RNA electrophoresis
pattern. These included 11 cases of MSA and 5 controls. Thus, the success rate for analysis of RNA from FFPE samples was 52% (26 of 50). For MSA, a total of 395 and 383 miRNAs were identified in the pons and cerebellum, respectively; 5 were upregulated and 33 were down-regulated in the pons and 5 were up-regulated and 18 were down-regulated in the cerebellum. Several miRNAs down-regulated in the pons (miR129-2-3p and miR-129-5p) and cerebellum (miR-129-2-3p, miR-129-5p and miR-1323p) had already been identified in frozen cerebellum from MSA patients. These findings suggest that archived FFPE postmortem samples can be a valuable source for miRNA profiling in MSA.
Keywords: bioinformatics, formalin-fixed paraffin-embedded specimen, microRNA, multiple system atrophy, pons, cerebellum
1. Introduction MicroRNAs (miRNAs) are small, single-stranded, noncoding RNAs that function as post-transcriptional regulators by binding to complementary sequences on target mRNA transcripts. A single miRNA may bind to hundreds of target genes [8] and therefore dysregulation in a single miRNA may have a wide range of biological processes including stress response, morphogenesis, synaptic plasticity, apoptosis, cell cycle control and neuroprotection [23]. The implications of dysregulation of miRNA networks have already been well demonstrated in the field of cancer research [22]. miRNAs are also implicated in the pathogenesis of neurodegenerative and psychiatric disorders [1,7,13,23] including Alzheimer’s disease [10,26,32], Parkinson’s disease [17],
amyotrophic lateral sclerosis (ALS) [6,26], Huntington’s disease [15,25] and multiple system atrophy (MSA) [18,28]. Postmortem frozen brain tissue has been employed in these cases. Moreover, cerebrospinal fluid [6,9], peripheral blood [6,9,29] and skeletal muscle [4] have also been analyzed for miRNA expression. The expression of miRNAs in formalin-fixed paraffin-embedded (FFPE) samples is known to be well correlated with that in fresh frozen samples [33]. Therefore, FFPE samples have been used for studies of miRNA in cancer [2,5,19,24]. Recently, we have shown that informative miRNA data can be derived from archived FFPE samples from postmortem cases of ALS and normal controls [31]. miRNA analysis has now been performed on FFPE samples of patients with MSA and neurologically normal controls. MSA is an adult-onset neurodegenerative disorder characterized clinically by a combination of various degrees of parkinsonism, cerebellar ataxia and autonomic dysfunction. Here we report that archived FFPE postmortem samples from affected regions of MSA can be a valuable source for miRNA profiling.
2. Materials and methods 2.1. Subjects Since our previous study demonstrated that the RNA yield was significantly higher in samples that had been fixed for a short period (3-4 weeks) than in those that had been fixed for a long period (8-16 weeks), we selected 50 FFPE samples that had been fixed for 3-4 weeks (Table 1). These included 13 cases of MSA (aged 57-82 years, mean 67.8 years) and 13 neurologically normal controls (aged 37-85 years, mean 69.9 years). All the diagnoses had been confirmed by neuropathological examination using immunohistochemistry for α-synuclein. The FFPE specimens employed were from the
pons and cerebellum of patients with MSA and normal subjects. In a case of MSA (case 5), only cerebellum was employed. In a control subject (case 14), only pons was employed. Autopsy was performed from 1 to 20 hours after death (mean 5.2 hours). The brains had been immersed in 10% or 20% formalin or 10% phosphate-buffered formalin. After fixation, the cerebrum had been cut into slices 10 mm thick in the coronal plane, and the brainstem and cerebellum had been cut transversely into slices 7 mm thick. Samples had then been removed from each slice and subjected to dehydration, clearing and impregnation on an automated instrument (Tissue-Tek VIP 5 Jr., Sakura Finetek Japan, Tokyo, Japan). After tissue processing, each specimen had been embedded in paraffin, and the paraffin blocks had been stored for 5-168 months (mean 72.5 months) at room temperature protected from air and sunlight. Twenty-six FFPE samples from 16 cases were selected for miRNA analysis on the basis of the criteria reported previously [31] (Table 1): (i) a formalin fixation time of less than 4 weeks, (ii) a total RNA yield per sample of more than 500 ng, and (iii) sufficient quality of the RNA electrophoresis pattern. These included 11 cases of MSA and 5 neurologically normal controls. Five cases of MSA were the olivopontocerebellar atrophy-predominant (MSA-C) type and 6 cases were the striatonigral degenerationpredominant (MSA-P) type. The involvement of the pons and cerebellum was indistinguishable between MSA-C and MSA-P (Table 2). This study was approved by the Institutional Ethics Committee of Hirosaki University Graduate School of Medicine, Japan.
2.2. RNA extraction Two 5-m-thick sections were cut from each block and placed in sterile 1.5-mL
centrifuge tubes ready for extraction. Total RNA including small RNAs was extracted using an Arcturus® Paradise® PLUS FFPE RNA Isolation Kit (Life Technologies Corporation, Carlsbad, CA, USA) as reported previously [31]. The degrees of RNA cross-linking and RNA degradation were analyzed by agarose gel electrophoresis using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). RNA yield was determined from the A260/A280 absorbance ratio using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
2.3. miRNA expression profiling Extracted samples of total RNA that satisfied our criteria were labeled with Hy5 using a miRCURY LNA Array miR labeling kit (Exiqon, Vedbaek, Denmark). The labeled RNAs were then hybridized onto a 3D-Gene human miRNA oligo chip (Toray Industries Inc., Tokyo, Japan). The annotation and oligonucleotide sequences of the probes conformed to the miRBase miRNA database Release 17v1.0.0. Nomenclature of miRNA was updated from Release 17 to Release 20 (http://www.mirbase.org/). After stringent washing, the fluorescent signals were scanned with a 3D-Gene Scanner (Toray Industries Inc.) and analyzed using 3D-Gene Extraction software (Toray Industries Inc.) as reported previously [31]. Any signal intensity level over 50 was considered to be significant. The signal was considered to be up-regulated when it was increased by 50% or more (>log2X =＋0.58) compared with the control signal level, and down-regulated when it was decreased by 50% or less (
2.4. miRNA targets and pathway analysis
Bioinformatics prediction of target genes and miRNA binding sites was performed using miRmap web-based open source software (http://mirmap.ezlab.org/) [30]. Canonical function and ontology analyses for candidate miRNA targets were performed using MetaCore Functional Analysis (ver.6.27, Thomson Reuter/GeneGo, http://lsresearch.thomsonreuters.com/).
2.5. Statistical analysis Values were expressed as mean ± standard error of the mean. Differences in postmortem interval, fixation time or storage period between MSA and controls were analyzed using Student’s t test. Probability values of p <0.05 were considered to be significant.
3. Results 3.1. Stability of RNA in postmortem samples We evaluated 50 samples for which the formalin fixation time had been less than 4 weeks. There was no difference in the postmortem interval or fixation time between MSA and controls; the mean of postmortem interval in MSA and controls was 5.4 and 5 hours, respectively, and the mean of fixation time in MSA and controls was 4 and 3.8 weeks, respectively. The mean of storage period tend to be longer in controls (89.1 months) than MSA (55.8 months), although the difference was not significant (p = 0.07). The RNA yield was more than 500 ng in 41 of the 50 samples, and the RNA agarose gel image was of sufficient quality in 26 of the 41 samples. Thus, the success rate for analysis of RNA from FFPE samples of the human postmortem brain was 52% (26 of 50). The success rate in MSA patients and control subjects was 72% (18 of 25)
and 32% (8 of 25), respectively. Based on the above step, 26 samples from 16 autopsy cases were selected for miRNA analysis; these included cases of MSA (n = 11) and neurologically normal controls (n = 5) (Table 1). Disease duration in patients with MSA ranged from 12 to 204 months (mean 89.3 months).
3.2. miRNA analysis and candidate target genes in MSA A total of 395 and 383 miRNAs were isolated from the pons and cerebellum, respectively, of patients with MSA. In MSA, 5 miRNAs were up-regulated (log2X > +0.58) and 33 were down-regulated (log2X >－0.58) in the pons, whereas 5 miRNAs were up-regulated and 18 were down-regulated in the cerebellum, relative to controls. These up- or down-regulated miRNAs in MSA are shown in Table 3. In the pons, miR-1290 was found to be the most highly up-regulated miRNA (+1.62-fold change), followed in order by miR-21-5p, miR-30b-5p, miR-4428, and miR-23a-3p as the top 5 up-regulated miRNAs. On the other hand, miR-219a-5p (+0.36-fold change), miR-379-5p, miR-138-5p, miR-127-3p, and miR-4687-5p were the top 5 down-regulated miRNAs. In the cerebellum, miR-4428 was found to be the most highly up-regulated miRNA (+1.92-fold change), followed in order by miR-4732-5p, miR-1290, miR-3619-3p, and miR-4725-3p as the top 5 up-regulated miRNAs. On the other hand, miR-129-2-3p (+0.33-fold change), miR-129-1-3p, miR-219a-2-3p, miR129-5p, and miR-3907 were the top 5 down-regulated miRNAs. miR-1290 and miR4428 were commonly up-regulated in the pons and cerebellum of MSA patients. The candidate target genes of the top 5 up-regulated and down-regulated miRNAs in MSA were respectively identified using miRmap web-based open source software.
Among the 50 identified candidate targets for each of the 5 miRNAs, those showing high matching scores and overlapping candidates were considered possible key targets at sites of pathology. The selected target genes are listed in Table 4.
3.3. Ontology analysis of predicted target genes for disease-specific miRNAs From the list of the target candidate genes, the top 50 candidates for each miRNA were annotated and a total of 250 candidates for 5 miRNAs were nominated as targets for ontology analysis. We subjected the 250 genes differentially expressed in MSA to MetaCore, functional network and ontology analysis. As shown in Table 5A, B, the biological processes altered by these target genes in the pons in MSA were found to be related to biosynthesis for both up-regulated and down-regulated miRNAs. In the cerebellum in MSA, the processes of metabolism and axonogenesis were found to be altered for up-regulated miRNAs, and regulation of phospholipase C activity was found to be altered for down-regulated miRNAs (Table 5C, D).
4. Discussion In the present study, we demonstrated that miRNAs extracted from FFPE samples of postmortem brain tissue from patients with MSA and normal controls were relatively well preserved; 26 of 50 samples (52%), for which the longest storage period was 14 years, provided RNA of sufficient quality. The success rate in the present study was almost equal to that in our previous study (46.2%) [31], in which we evaluated FFPE samples from the motor cortex of patients with ALS and normal controls. Thus, miRNAs appear to be relatively stable in FFPE samples, even those from postmortem specimens. However, the success rate in normal controls (32%) was lower than that in
MSA (72%). There was no difference in the postmortem interval or fixation time between MSA and controls. By contrast, the storage period tend to be longer in controls (mean 89.1 months) than MSA (mean 55.8 months), although the difference was not significant (p = 0.07). Longer storage in paraffin may influence the quality of miRNA. Another possibility is that neurodegenerative disorders are linked to progressive loss of neurons and reaction of glial cells (astrocyte and microglia); hence, even if the same area has been selected between affected individuals and normal controls, the composition of the sample could not be similar in term of neuronal and non-neuronal elements [14]. Ubhi et al. [28] investigated miRNA profiles in cases of MSA (n = 3) in comparison with controls (n = 4) using frozen samples of frontal cortex; 214 miRNAs were upregulated and none were down-regulated in comparison with controls. In the present study, 5 miRNAs were up-regulated and 33 were down-regulated in the pons and 5 miRNAs were up-regulated and 18 were down-regulated in the cerebellum of patients with MSA relative to controls. None of the miRNAs found to be up-regulated in our study were up-regulated in the study of Ubhi et al. [28]. On the other hand, 6 of 33 miRNAs down-regulated in the pons as well as 5 of 18 miRNAs down-regulated in the cerebellum in our study were up-regulated in the study of Ubhi et al. [28]. The discrepancy of the results between our study and that of Ubhi et al. may have been attributable to the difference in the samples employed (frozen vs FFPE) or the brain area examined (frontal cortex vs pons and cerebellum). Further studies using larger patient cohorts will be necessary to clarify whether such changes in miRNA expression are global and present in all brain areas, including both affected and unaffected regions. It is noteworthy that in our study 2 miRNAs (miR-1290 and miR-4428) were up-
regulated and 7 miRNAs (miR-129-2-3p, miR-1233-3p, miR-3663-5p, miR-4739, miR4440, miR-129-5p and miR-138-5p) were down-regulated in both the pons and the cerebellum. These findings suggest that pathological process in the pons is similar to that in the cerebellum in MSA. Most of the functions and target genes of these miRNAs in the brain are unknown. miR-1290 might play important roles in neuronal differentiation [35,37]. miR-129-5p might regulate the FMR1 gene, which is silenced in fragile X syndrome, an inheritable neuropsychological disease [38]. miR-138-5p participates in the regulation of oligodendrocyte differentiation and myelin maintenance, as well as in the pathogenesis of demyelination-related diseases such as multiple sclerosis and leukodystrophy [20]. It is important to note that MSA is characterized pathologically by the accumulation of abnormal -synuclein in oligodendrocytes and secondary demyelination [27]. Recently, Lee et al. [18] examined the expression of miRNA in cases of MSA (n = 4) and controls (n = 4) using frozen samples of cerebellum; 9 miRNAs (miR-129-2-3p, miR-129-5p, miR-337-3p, miR-380, miR-433, miR-132-3p, miR-410, miR-206 and miR-409-5p) were down-regulated and 2 (miR-202 and miR-199a-5p) were upregulated in comparison with controls. Three miRNAs (miR-129-2-3p, miR-129-5p and miR-132-3p) found to be down-regulated in the cerebellum in our study were also down-regulated in the study of Lee et al. [18]. In addition, 2 miRNAs (miR-129-2-3p and miR-129-5p) found to be down-regulated in the pons in our study were also downregulated in the study of Lee et al. [18]. Particularly, miR-129-2-3p and miR-129-5p were down-regulated in both the pons and the cerebellum. miR-129-2-3p is one of the metabolic stress-induced miRNAs [16]. miR-129-5p might play roles in the regulation of autophagy in atherosclerosis [11] and cancer [21,34]. miR-132-3p is one of the well-
studied brain-specific miRNAs, which plays a pivotal role in synaptogenesis, synaptic plasticity and structural remodeling [36]. Furthermore, miR-132-3p is known to be down-regulated in the frontal and temporal cortex, hippocampus and cerebellum in patients with Alzheimer’s disease [14]. Interestingly, miR-132-3p is also deregulated in the brain of patients with Parkinson’s [3] and Huntington’s diseases [15]. miR-132-3p is also down-regulated in the brainstem area in -synuclein-transgenic mouse [12]. These findings suggest that miR-132-3p shares common pathomechanisms in certain neurodegenerative disorders. In conclusion, archived FFPE postmortem samples from affected brain regions can be a valuable source for miRNA profiling in neurodegenerative disorders. Targeting neuroprotective pathways controlled by miRNAs may represent a therapeutic strategy for the treatment of neurodegenerative disorders.
Ackonwledgements This work was supported by JSPS KAKENHI Grant Numbers 16K15473 (K.W.) and 26430049 (F.M.), the Collaborative Research Project (2015-2508) of the Brain Research Institute, Niigata University (F.M.), a Grant from Japan Science and Technology Program (AS2314204F) (H.S., K.W.) and the Research Committee for Ataxic Disease (H.S., K.W.) from the Ministry of Health, Labour and Welfare, Japan. The authors wish to express their gratitude to M. Nakata, A. Ono and Y. Hama for technical assistance.
References 1. C. Barbato, F. Ruberti, C. Cogoni, Searching for MIND: microRNAs in neurodegenerative diseases, J. Biomed. Biotechnol. 2009 (2009) 871313. 2. Z. Bing, S.R. Master, J.W. Tobias, D.A. Baldwin, X.W. Xu, J.E. Tomaszewski, MicroRNA expression profiles of seminoma from paraffin-embedded formalin-fixed tissue, Virchows Arch. 461 (2012) 663-668. 3. C.E. Briggs, Y. Wang, B. Kong, T.U. Woo, L.K. Iyer, K.C. Sonntag, Midbrain dopamine neurons in Parkinson’s disease exhibit a dysregulated miRNA and targetgene network, Brain Res. 1618 (2015) 111-121. 4. G. Bruneteau, T. Simonet, S. Bauché, N. Mandjee, E. Malfatti, E. Girard, M.L. Tanguy, A. Behin, F. Khiami, E. Sariali, C. Hell-Remy, F. Salachas, P.F. Pradat, E. Fournier, L. Lacomblez, J. Koenig, N.B. Romero, B. Fontaine, V. Meininger, L. Schaeffer, D. Hantaï, Muscle histone deacetylase 4 upregulation in amyotrophic lateral sclerosis: potential role in reinnervation ability and disease progression, Brain 136(Pt 8) (2013) 2359-2368. 5. L. Chen, Y. Li, Y. Fu, J. Peng, M.H. Mo, M. Stamatakos, C.B. Teal, R.F. Brem, A. Stojadinovic, M. Grinkemeyer, T.A. McCaffrey, Y.G. Man, S.W. Fu, Role of deregulated microRNAs in breast cancer progression using FFPE tissue, PLoS. One 8 (2013) e54213. 6. B. De Felice, A. Annunziata, G. Fiorentino, M. Borra, E. Biffali, C. Coppola, R. Cotrufo, J. Brettschneider, M.L. Giordana, T. Dalmay, G. Wheeler, R. D’Alessandro,
miR-338-3p is over-expressed in blood, CFS, serum and spinal cord from sporadic amyotrophic lateral sclerosis patients, Neurogenetics 15 (2014) 243-253. 7. A.M. Enciu, B.O. Popescu, A. Gheorghisan-Galateanu, MicroRNAs in brain development and degeneration, Mol. Biol. Rep. 39 (2012) 2243-2252. 8. A. Esquela-Kerscher, F.J. Slack, Oncomirs - microRNAs with a role in cancer, Nat. Rev. Cancer 6 (2006) 259-269. 9. A. Freischmidt, K. Müller, A.C. Ludolph, J.H. Weishaupt, Systemic dysregulation of TDP-43 binding microRNAs in amyotrophic lateral sclerosis, Acta Neuropathol. Commun. 1 (2013) 42. 10. H. Geekiyanage, C. Chan, MicroRNA-137/181c regulates serine palmitoyltransferase and in turn amyloid β, novel targets in sporadic Alzheimer’s disease, J. Neurosci. 31 (2011) 14820-14830. 11. Z. Geng, F. Xu, Y. Zhang, MiR-129-5p-mediated Beclin-1 suppression inhibits endothelial cell autophagy in atherosclerosis, Am. J. Transl. Res. 8 (2016) 18861894. 12. F. Gillardon, M. Mack, W. Rist, C. Schnack, M. Lenter, T. Hildebrandt, B. Hengerer, MicroRNA and proteome expression profiling in early-symptomatic α-synuclein (A30P)-transgenic mice, Proteomics Clin. Appl. 2 (2008) 697-705. 13. S.S. Hébert, B. De Strooper, Alterations of the microRNA network cause neurodegenerative disease, Trends Neurosci. 32 (2009) 199-206. 14. J. Hernandez-Rapp, S. Rainone, S.S. Hébert, MicroRNAs underlying memory deficits in neurodegenerative disorders, Prog. Neuropsychopharmacol. Biol. Psychiatry 2016 Apr 23. doi: 10.1016/j.pnpbp.2016.04.011. [Epub ahead of print] 15. R. Johnson, C. Zuccato, N.D. Belyaev, D.J. Guest, E. Cattaneo, N.J. Buckley, A
microRNA-based gene dysregulation pathway in Huntington’s disease, Neurobiol. Dis. 29 (2008) 438-445. 16. S. Kálmán, K.A. Garbett, A. Vereczkei, R.C. Shelton, Z. Korade, K. Mirnics, Metabolic stress-induced microRNA and mRNA expression profiles of human fibroblasts, Exp. Cell Res. 320 (2014) 343-353. 17. J. Kim, K. Inoue, J. Ishii, W.B. Vanti, S.V. Voronov, E. Murchison, G. Hannon, A. Abeliovich, A microRNA feedback circuit in midbrain dopamine neurons, Science 317 (2007) 1220-1224. 18. S.T. Lee, K. Chu, K.H. Jung, J.J. Ban, W.S. Im, H.Y. Jo, J.H. Park, J.Y. Lim, J.W. Shin, J. Moon, S.K. Lee, M. Kim, J.K. Roh, Altered expression of miR-202 in cerebellum of multiple-system atrophy, Mol. Neurobiol. 51 (2015) 180-186. 19. T.S. Lee, H.W. Jeon, Y.B. Kim, Y.A. Kim, M.A. Kim, S.B. Kang, Aberrant microRNA expression in endometrial carcinoma using formalin-fixed paraffinembedded (FFPE) tissues, PLoS. One 8 (2013) e81421. 20. J.S. Li, Z.X. Yao, MicroRNAs: novel regulators of oligodendrocyte differentiation and potential therapeutic targets in demyelination-related diseases, Mol. Neurobiol. 45 (2012) 200-212. 21. J. Luo, J. Chen, L. He, mir-129-5p attenuates irradiation-induced autophagy and decreases radioresistance of breast cancer cells by targeting HMGB1, Med. Sci. Monit. 21 (2015) 4122-4129. 22. L. Ma, R.A. Weinberg, Micromanagers of malignancy: role of microRNAs in regulating metastasis, Trends Genet. 24 (2008) 448-456. 23. P.T. Nelson, W.X. Wang, B.W. Rajeev, MicroRNAs (miRNAs) in neurodegenerative diseases, Brain Pathol. 18 (2008) 130-138.
24. S. Osawa, Y. Shimada, S. Sekine, T. Okumura, T. Nagata, J. Fukuoka, K. Tsukada, MicroRNA profiling of gastric cancer patients from formalin-fixed paraffinembedded samples, Oncol. Lett. 2 (2011) 613-619. 25. A.N. Packer, Y. Xing, S.Q. Harper, L. Jones, B.L. Davidson, The bifunctional microRNA miR-9/miR-9* regulates REST and CoREST and is downregulated in Huntington’s disease, J. Neurosci. 28 (2008) 14341-14346. 26. M. Shioya, S. Obayashi, H. Tabunoki, K. Arima, Y. Saito, T. Ishida, J. Satoh, Aberrant microRNA expression in the brains of neurodegenerative diseases: miR29a decreased in Alzheimer disease brains targets neurone navigator 3, Neuropathol. Appl. Neurobiol. 36 (2010) 320-330. 27. N. Stefanova, G.K. Wenning, Review: Multiple system atrophy: emerging targets for interventional therapies, Neuropathol. Appl. Neurobiol. 42 (2016) 20-32. 28. K. Ubhi, E. Rockenstein, C. Kragh, C. Inglis, B. Spencer, S. Michael, M. Mante, A. Adame, D. Galasko, E. Masliah, Widespread microRNA dysregulation in multiple system atrophy - disease-related alteration in miR-96, Eur. J. Neurosci. 39 (2014) 1026-1041. 29. A. Vallelunga, M. Ragusa, S. Di Mauro, T. Iannitti, M. Pilleri, R. Biundo, L. Weis, C. Di Pietro, A. De Iuliis, A. Nicoletti, M. Zappia, M. Purrello, A. Antonini, Identification of circulating microRNAs for the differential diagnosis of Parkinson’s disease and multiple system atrophy, Front. Cell Neurosci. 8 (2014) 156. 30. C.E. Vejnar, E.M. Zdobnov, miRmap: Comprehensive prediction of microRNA target repression strength, Nucleic Acids Res. 40 (2012) 11673-11683. 31. K. Wakabayashi, F. Mori, A. Kakita, H. Takahashi, J. Utsumi, H. Sasaki, Analysis of microRNA from archived formalin-fixed paraffin-embedded specimens of
amyotrophic lateral sclerosis, Acta Neuropathol. Commun. 2 (2014) 173. 32. W.X. Wang, Q. Huang, Y. Hu, A.J. Stromberg, P.T. Nelson, Patterns of microRNA expression in normal and early Alzheimer’s disease human temporal cortex: white matter versus gray matter, Acta Neuropathol. 121 (2011) 193-205. 33. Y. Xi, G. Nakajima, E. Gavin, C.G. Morris, K. Kudo, K. Hayashi, J. Ju, Systematic analysis of microRNA expression of RNA extracted from fresh frozen and formalinfixed paraffin-embedded samples, RNA 13 (2007) 1668-1674. 34. W. Xiao, B. Dai, Y. Zhu, D. Ye, Norcantharidin induces autophagy-related prostate cancer cell death through Beclin-1 upregulation by miR-129-5p suppression, Tumour Biol. 2015 Dec 5. [Epub ahead of print] 35. S.V. Yelamanchili, B. Morsey, E.B. Harrison, D.A. Rennard, K. Emanuel, I. Thapa, D.R. Bastola, H.S. Fox, The evolutionary young miR-1290 favors mitotic exit and differentiation of human neural progenitors through altering the cell cycle proteins, Cell Death Dis. 5 (2014) e982. 36. A. Yoshimura, T. Numakawa, H. Odaka, N. Adachi, Y. Tamai, H. Kunugi, Negative regulation of microRNA-132 in expression of synaptic proteins in neuronal differentiation of embryonic neural stem cells, Neurochem. Int. 97 (2016) 26-33. 37. H. Zhuang, R. Zhang, S. Zhang, Q. Shu, D. Zhang, G. Xu, Altered expression of microRNAs in the neuronal differentiation of human Wharton’s Jelly mesenchymal stem cells, Neurosci. Lett. 600 (2015) 69-74. 38. S. Zongaro, R. Hukema, S. D’Antoni, L. Davidovic, P. Barbry, M.V. Catania, R. Willemsen, B. Mari, B. Bardoni, The 3’ UTR of FMR1 mRNA is a target of miR101, miR-129-5p and miR-221: implications for the molecular pathology of FXTAS at the synapse, Hum. Mol. Genet. 22 (2013) 1971-1982.
39. Table 1 Summary of formalin-fixed paraffin-embedded samples used for RNA isolation.
Cerebellu Disease Cas
Pathologica
e
l
No.
diagnosis
Fixatio
Storage
Pons m
Postmorte Age/gende
duration
n
period
m interval r
(months
time (weeks)
RNA
microRN
yield
qualit
A
(ng)
y
analysis
(months
(hours) )
RNA
RNA
microRN
qualit
A
y
analysis
RNA yield
)
(ng)
1
MSA-C
57/F
18
20
4
101
1326
S
Yes
1940
S
Yes
2
MSA-C
71/F
12
13
4
92
1137
S
Yes
2250
S
Yes
3
MSA-P
66/M
108
2
4
85
1031
S
Yes
1470
S
Yes
4
MSA-P
61/M
144
3
4
8
259
I
No
504
S
Yes
5
MSA-C
66/M
96
3
4
32
NA
NA
7144
S
Yes
6
MSA-P
64/F
96
4
4
12
2970
S
Yes
5630
S
Yes
7
MSA-P
62/F
204
4.5
4
36
280
I
No
920
S
Yes
8
MSA-C
78/M
84
3
4
36
2840
S
Yes
656
S
Yes
9
MSA-C
82/M
80
2.5
4
48
970
S
Yes
9540
S
Yes
10
MSA-P
67/F
104
2.5
4
48
780
S
Yes
7550
S
Yes
11
MSA-P
75/M
96
3
4
72
1260
I
No
8690
I
No
12
MSA-P
65/F
84
4
4
72
4140
I
No
4250
I
No
13
MSA-P
68/M
36
6
4
84
560
I
No
4780
S
Yes
14
Control
37/M
12
3
101
37
I
No
NA
NA
15
Control
71/F
10
3
86
735
S
Yes
3740
S
Yes
16
Control
84/M
10
4
64
617
S
Yes
1280
S
Yes
17
Control
67/F
4
4
38
4610
S
Yes
1640
S
Yes
18
Control
85/F
1
4
5
49
I
No
668
I
No
19
Control
60/F
2
4
12
290
I
No
11620
I
No
20
Control
76/M
3
4
60
2290
I
No
11650
I
No
21
Control
51/M
6
4
72
230
I
No
8530
I
No
22
Control
82/M
6
4
168
1090
I
No
220
I
No
23
Control
55/F
4
4
108
90
I
No
9720
I
No
24
Control
79/F
2.5
4
168
3540
S
Yes
12560
I
No
25
Control
80/M
3
4
144
490
I
No
6890
S
Yes
26
Control
82/M
2
4
132
2690
I
No
4150
I
No
MSA-C, multiple system atrophy-cerebellar; MSA-P, multiple system atrophy-parkinsonism; S, sufficient; I, insufficient; NA, not available.
Table 2 Pathological finding in cases of MSA for microRNA study.
Case
Pathological
Neuronal loss Neuronal loss
No.
diagnosis
in pons
in cerebellum
1
MSA-C
1
1
2
MSA-C
1
1
3
MSA-P
2
2
4
MSA-P
2
2
5
MSA-C
3
3
6
MSA-P
2
2
7
MSA-P
3
3
8
MSA-C
3
3
9
MSA-C
3
3
10
MSA-P
3
3
13
MSA-P
1
1
Neuronal loss: 1 mild, 2 moderate, 3 severe.
Table 3 MicroRNAs up- and down-regulated in the pons and cerebellum in MSA.
Pons
Up-regulated
Down-regulated
Cerebellum
miRNA name
Ratio
miRNA name
Ratio
miR-1290
1.62
miR-4428
1.92
miR-21-5p
1.59
miR-4732-5p
1.79
miR-30b-5p
1.58
miR-1290
1.73
miR-4428
1.52
miR-3619-3p
1.54
miR-23a-3p
1.51
miR-4725-3p
1.5
miR-128-3p
0.67
miR-4739
0.67
miR-371b-3p
0.66
miR-4726-3p
0.66
miR-3928-3p
0.66
miR-1228-3p
0.66
miR-1915-3p
0.66
miR-346
0.65
miR-129-2-3p
0.65
miR-134-5p
0.64
miR-1203
0.65
miR-1233-3p
0.64
miR-584-5p
0.65
miR-484
0.63
miR-1910-5p
0.65
miR-138-5p
0.62
miR-675-5p
0.64
miR-132-3p
0.61
miR-149-5p
0.64
miR-3663-5p
0.61
miR-1233-3p
0.64
miR-4440
0.61
miR-3173-5p
0.63
miR-3184-5p
0.6
miR-1539
0.63
miR-557
0.59
miR-513a-5p
0.62
miR-3907
0.56
miR-3663-5p
0.62
miR-129-5p
0.46
miR-4723-3p
0.61
miR-219a-2-3p
0.43
miR-4739
0.61
miR-129-1-3p
0.37
miR-4440
0.61
miR-129-2-3p
0.33
miR-1909-5p
0.6
miR-129-5p
0.6
miR-330-5p
0.59
miR-572
0.59
miR-4632-3p
0.57
miR-940
0.56
miR-1231
0.55
miR-124-3p
0.53
miR-34a-5p
0.51
miR-210-3p
0.51
miR-4687-5p
0.51
miR-127-3p
0.5
miR-138-5p
0.48
miR-379-5p
0.46
miR-219a-5p
0.36
Table 4 Predicted target genes for MSA-specific miRNAs identified by miRmap web-based open
source software.
Gene
Gene
symbol
ID
Gene name
bone morphogenetic protein receptor, For up-regulated miRNA
BMPR2
659 type II suppressor of glucose, autophagy
in pons
SOGA1
140710 associated 1
PRLR
5618
prolactin receptor G protein-coupled receptor, family C, group 5,
GPRC5A
9052 member A cAMP responsive element binding
CREB5
9586 protein 5
For
down-regulated
glycosyltransferase GXYLT1
miRNA in pons
8
domain
283464 containing 3
ELMOD2
255520
NFAT5
10725
ELMO/CED-12 domain containing 2 nuclear factor of activated T-cells 5, tonicityresponsive
ZNF207
7756
SOGA3
387104
zinc finger protein 207 suppressor of glucose, autophagy associated, family member 3 protein phosphatase 1, regulatory (inhibitor)
For up-regulated miRNA
PPP1R12B 4660 subunit 12B
in cerebellum
SSR1
6745
signal sequence receptor, alpha LIM domain containing preferred translocation partner in
LPP
4026 lipoma RAB3B, member RAS oncogene
RAB3B
5865 family
For
ZNF704
619279
zinc finger protein 704
CDK6
1021
cyclin-dependent kinase 6
CENPP
401541
centromere protein P
SIM1
6492
single-minded homolog 1 (Drosophila)
SESTD1
228071
SEC14 and spectrin domains 1
STXBP4
252983
syntaxin binding protein 4
down-regulated
miRNA in cerebellum
Table 5 Ontology analysis of predicted target genes for disease-specific miRNAs in MSA.
(A)
GO Processes of target genes for up-regulated miRNAs in pons
p-value
FDR
1
regulation of cellular macromolecule biosynthetic process
6.93E-13
2.14E-09
2
regulation of macromolecule biosynthetic process
1.05E-12
2.14E-09
3
transcription, DNA-templated
2.93E-12
3.09E-09
4
regulation of transcription, DNA-templated
3.52E-12
3.09E-09
5
regulation of RNA metabolic process
3.80E-12
3.09E-09
6
regulation of RNA biosynthetic process
6.56E-12
3.82E-09
7
regulation of cellular process
6.58E-12
3.82E-09
8
biological regulation
1.22E-11
6.11E-09
9
regulation of nucleobase-containing compound metabolic process 1.35E-11
6.11E-09
10
RNA biosynthetic process
6.17E-09
(B)
1.52E-11
GO Processes of target genes for down-regulated miRNAs in pons p-value
FDR
1
organic substance biosynthetic process
1.14E-09
5.55E-06
2
biosynthetic process
3.19E-09
7.16E-06
3
cellular biosynthetic process
4.40E-09
7.16E-06
4
regulation of multicellular organismal development
1.10E-07
1.27E-04
5
nervous system development
1.84E-07
1.27E-04
6
macromolecule biosynthetic process
1.86E-07
1.27E-04
7
regulation of cell development
2.08E-07
1.27E-04
8
cellular macromolecule biosynthetic process
2.09E-07
1.27E-04
9
rhythmic process
3.11E-07
1.58E-04
10
cell-cell signaling
3.30E-07
1.58E-04
p-value
FDR
GO Processes of target genes for up-regulated miRNAs in (C) cerebellum 1
positive regulation of biological process
3.58E-10
1.25E-06
2
regulation of cellular metabolic process
1.28E-09
2.24E-06
3
central nervous system projection neuron axonogenesis
3.50E-09
3.13E-06
4
regulation of metabolic process
3.57E-09
3.13E-06
5
regulation of primary metabolic process
1.53E-08
1.07E-05
6
regulation of nitrogen compound metabolic process
1.99E-08
1.14E-05
7
biological regulation
2.47E-08
1.14E-05
8
cell morphogenesis involved in differentiation
3.35E-08
1.14E-05
9
positive regulation of cellular process
3.53E-08
1.14E-05
10
central nervous system neuron axonogenesis
3.53E-08
1.14E-05
p-value
FDR
GO Processes of target genes for down-regulated miRNAs in (D) cerebellum 1
positive regulation of phospholipase C activity
1.19E-12
4.53E-09
2
regulation of phospholipase C activity
1.92E-12
4.53E-09
3
positive regulation of phospholipase activity
3.20E-12
5.04E-09
4
positive regulation of lipase activity
1.81E-11
1.71E-08
5
regulation of phospholipase activity
1.81E-11
1.71E-08
6
establishment of localization
3.61E-11
2.84E-08
7
negative regulation of glucose transport
4.93E-11
3.33E-08
8
regulation of smooth muscle cell proliferation
6.23E-11
3.68E-08
9
localization
1.02E-10
5.32E-08
10
regulation of transport
1.32E-10
6.21E-08
p-value, the probability of a random intersection of 2 different gene/protein/compound sets; FDR, false discovery rate.
40.
S0304-3940(16)30786-8 http://dx.doi.org/doi:10.1016/j.neulet.2016.10.034 NSL 32370
To appear in:
Neuroscience Letters
Received date: Revised date: Accepted date:
29-8-2016 6-10-2016 19-10-2016
Please cite this article as: Koichi Wakabayashi, Fumiaki Mori, Akiyoshi Kakita, Hitoshi Takahashi, Shinya Tanaka, Jun Utsumi, Hidenao Sasaki, MicroRNA expression profiles of multiple system atrophy from formalin-fixed paraffin-embedded samples, Neuroscience Letters http://dx.doi.org/10.1016/j.neulet.2016.10.034 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Research article
MicroRNA expression profiles of multiple system atrophy from formalin-fixed paraffin-embedded samples
Koichi Wakabayashia, *, Fumiaki Moria, Akiyoshi Kakitab, Hitoshi Takahashic, Shinya Tanakad, Jun Utsumie, Hidenao Sasakie
a
Department of Neuropathology, Institute of Brain Science, Hirosaki University
Graduate School of Medicine, Hirosaki 036-8562, Japan b
Department of Pathological Neuroscience, Center for Bioresource-based Researches,
Brain Research Institute, University of Niigata, Niigata 951-8585, Japan c
Department of Pathology, Brain Research Institute, University of Niigata, Niigata 951-
8585, Japan d
Department of Cancer Pathology, Hokkaido University Graduate School of Medicine,
Sapporo 060-8638, Japan e
Department of Neurology, Hokkaido University Graduate School of Medicine, Sapporo
060-8638, Japan
*
Corresponding author.
E-mail address: [email protected] (K. Wakabayashi).
HIGHLIGHT
MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression.
Multiple system atrophy (MSA) is an adult-onset neurodegenerative disorder.
We evaluated 50 formalin-fixed paraffin-embedded (FFPE) samples from patients with MSA.
Archived FFPE postmortem samples can be a valuable source for miRNA profiling in MSA.
ABSTRACT MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression. Recently, we have shown that informative miRNA data can be derived from archived formalin-fixed paraffin-embedded (FFPE) samples from postmortem cases of amyotrophic lateral sclerosis and normal controls. miRNA analysis has now been performed on FFPE samples from affected brain regions in patients with multiple system atrophy (MSA) and the same areas in neurologically normal controls. We evaluated 50 samples from patients with MSA (n = 13) and controls (n = 13). Twentysix samples were selected for miRNA analysis on the basis of the criteria reported previously: (i) a formalin fixation time of less than 4 weeks, (ii) a total RNA yield per sample of more than 500 ng, and (iii) sufficient quality of the RNA electrophoresis
pattern. These included 11 cases of MSA and 5 controls. Thus, the success rate for analysis of RNA from FFPE samples was 52% (26 of 50). For MSA, a total of 395 and 383 miRNAs were identified in the pons and cerebellum, respectively; 5 were upregulated and 33 were down-regulated in the pons and 5 were up-regulated and 18 were down-regulated in the cerebellum. Several miRNAs down-regulated in the pons (miR129-2-3p and miR-129-5p) and cerebellum (miR-129-2-3p, miR-129-5p and miR-1323p) had already been identified in frozen cerebellum from MSA patients. These findings suggest that archived FFPE postmortem samples can be a valuable source for miRNA profiling in MSA.
Keywords: bioinformatics, formalin-fixed paraffin-embedded specimen, microRNA, multiple system atrophy, pons, cerebellum
1. Introduction MicroRNAs (miRNAs) are small, single-stranded, noncoding RNAs that function as post-transcriptional regulators by binding to complementary sequences on target mRNA transcripts. A single miRNA may bind to hundreds of target genes [8] and therefore dysregulation in a single miRNA may have a wide range of biological processes including stress response, morphogenesis, synaptic plasticity, apoptosis, cell cycle control and neuroprotection [23]. The implications of dysregulation of miRNA networks have already been well demonstrated in the field of cancer research [22]. miRNAs are also implicated in the pathogenesis of neurodegenerative and psychiatric disorders [1,7,13,23] including Alzheimer’s disease [10,26,32], Parkinson’s disease [17],
amyotrophic lateral sclerosis (ALS) [6,26], Huntington’s disease [15,25] and multiple system atrophy (MSA) [18,28]. Postmortem frozen brain tissue has been employed in these cases. Moreover, cerebrospinal fluid [6,9], peripheral blood [6,9,29] and skeletal muscle [4] have also been analyzed for miRNA expression. The expression of miRNAs in formalin-fixed paraffin-embedded (FFPE) samples is known to be well correlated with that in fresh frozen samples [33]. Therefore, FFPE samples have been used for studies of miRNA in cancer [2,5,19,24]. Recently, we have shown that informative miRNA data can be derived from archived FFPE samples from postmortem cases of ALS and normal controls [31]. miRNA analysis has now been performed on FFPE samples of patients with MSA and neurologically normal controls. MSA is an adult-onset neurodegenerative disorder characterized clinically by a combination of various degrees of parkinsonism, cerebellar ataxia and autonomic dysfunction. Here we report that archived FFPE postmortem samples from affected regions of MSA can be a valuable source for miRNA profiling.
2. Materials and methods 2.1. Subjects Since our previous study demonstrated that the RNA yield was significantly higher in samples that had been fixed for a short period (3-4 weeks) than in those that had been fixed for a long period (8-16 weeks), we selected 50 FFPE samples that had been fixed for 3-4 weeks (Table 1). These included 13 cases of MSA (aged 57-82 years, mean 67.8 years) and 13 neurologically normal controls (aged 37-85 years, mean 69.9 years). All the diagnoses had been confirmed by neuropathological examination using immunohistochemistry for α-synuclein. The FFPE specimens employed were from the
pons and cerebellum of patients with MSA and normal subjects. In a case of MSA (case 5), only cerebellum was employed. In a control subject (case 14), only pons was employed. Autopsy was performed from 1 to 20 hours after death (mean 5.2 hours). The brains had been immersed in 10% or 20% formalin or 10% phosphate-buffered formalin. After fixation, the cerebrum had been cut into slices 10 mm thick in the coronal plane, and the brainstem and cerebellum had been cut transversely into slices 7 mm thick. Samples had then been removed from each slice and subjected to dehydration, clearing and impregnation on an automated instrument (Tissue-Tek VIP 5 Jr., Sakura Finetek Japan, Tokyo, Japan). After tissue processing, each specimen had been embedded in paraffin, and the paraffin blocks had been stored for 5-168 months (mean 72.5 months) at room temperature protected from air and sunlight. Twenty-six FFPE samples from 16 cases were selected for miRNA analysis on the basis of the criteria reported previously [31] (Table 1): (i) a formalin fixation time of less than 4 weeks, (ii) a total RNA yield per sample of more than 500 ng, and (iii) sufficient quality of the RNA electrophoresis pattern. These included 11 cases of MSA and 5 neurologically normal controls. Five cases of MSA were the olivopontocerebellar atrophy-predominant (MSA-C) type and 6 cases were the striatonigral degenerationpredominant (MSA-P) type. The involvement of the pons and cerebellum was indistinguishable between MSA-C and MSA-P (Table 2). This study was approved by the Institutional Ethics Committee of Hirosaki University Graduate School of Medicine, Japan.
2.2. RNA extraction Two 5-m-thick sections were cut from each block and placed in sterile 1.5-mL
centrifuge tubes ready for extraction. Total RNA including small RNAs was extracted using an Arcturus® Paradise® PLUS FFPE RNA Isolation Kit (Life Technologies Corporation, Carlsbad, CA, USA) as reported previously [31]. The degrees of RNA cross-linking and RNA degradation were analyzed by agarose gel electrophoresis using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). RNA yield was determined from the A260/A280 absorbance ratio using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
2.3. miRNA expression profiling Extracted samples of total RNA that satisfied our criteria were labeled with Hy5 using a miRCURY LNA Array miR labeling kit (Exiqon, Vedbaek, Denmark). The labeled RNAs were then hybridized onto a 3D-Gene human miRNA oligo chip (Toray Industries Inc., Tokyo, Japan). The annotation and oligonucleotide sequences of the probes conformed to the miRBase miRNA database Release 17v1.0.0. Nomenclature of miRNA was updated from Release 17 to Release 20 (http://www.mirbase.org/). After stringent washing, the fluorescent signals were scanned with a 3D-Gene Scanner (Toray Industries Inc.) and analyzed using 3D-Gene Extraction software (Toray Industries Inc.) as reported previously [31]. Any signal intensity level over 50 was considered to be significant. The signal was considered to be up-regulated when it was increased by 50% or more (>log2X =＋0.58) compared with the control signal level, and down-regulated when it was decreased by 50% or less (
2.4. miRNA targets and pathway analysis
Bioinformatics prediction of target genes and miRNA binding sites was performed using miRmap web-based open source software (http://mirmap.ezlab.org/) [30]. Canonical function and ontology analyses for candidate miRNA targets were performed using MetaCore Functional Analysis (ver.6.27, Thomson Reuter/GeneGo, http://lsresearch.thomsonreuters.com/).
2.5. Statistical analysis Values were expressed as mean ± standard error of the mean. Differences in postmortem interval, fixation time or storage period between MSA and controls were analyzed using Student’s t test. Probability values of p <0.05 were considered to be significant.
3. Results 3.1. Stability of RNA in postmortem samples We evaluated 50 samples for which the formalin fixation time had been less than 4 weeks. There was no difference in the postmortem interval or fixation time between MSA and controls; the mean of postmortem interval in MSA and controls was 5.4 and 5 hours, respectively, and the mean of fixation time in MSA and controls was 4 and 3.8 weeks, respectively. The mean of storage period tend to be longer in controls (89.1 months) than MSA (55.8 months), although the difference was not significant (p = 0.07). The RNA yield was more than 500 ng in 41 of the 50 samples, and the RNA agarose gel image was of sufficient quality in 26 of the 41 samples. Thus, the success rate for analysis of RNA from FFPE samples of the human postmortem brain was 52% (26 of 50). The success rate in MSA patients and control subjects was 72% (18 of 25)
and 32% (8 of 25), respectively. Based on the above step, 26 samples from 16 autopsy cases were selected for miRNA analysis; these included cases of MSA (n = 11) and neurologically normal controls (n = 5) (Table 1). Disease duration in patients with MSA ranged from 12 to 204 months (mean 89.3 months).
3.2. miRNA analysis and candidate target genes in MSA A total of 395 and 383 miRNAs were isolated from the pons and cerebellum, respectively, of patients with MSA. In MSA, 5 miRNAs were up-regulated (log2X > +0.58) and 33 were down-regulated (log2X >－0.58) in the pons, whereas 5 miRNAs were up-regulated and 18 were down-regulated in the cerebellum, relative to controls. These up- or down-regulated miRNAs in MSA are shown in Table 3. In the pons, miR-1290 was found to be the most highly up-regulated miRNA (+1.62-fold change), followed in order by miR-21-5p, miR-30b-5p, miR-4428, and miR-23a-3p as the top 5 up-regulated miRNAs. On the other hand, miR-219a-5p (+0.36-fold change), miR-379-5p, miR-138-5p, miR-127-3p, and miR-4687-5p were the top 5 down-regulated miRNAs. In the cerebellum, miR-4428 was found to be the most highly up-regulated miRNA (+1.92-fold change), followed in order by miR-4732-5p, miR-1290, miR-3619-3p, and miR-4725-3p as the top 5 up-regulated miRNAs. On the other hand, miR-129-2-3p (+0.33-fold change), miR-129-1-3p, miR-219a-2-3p, miR129-5p, and miR-3907 were the top 5 down-regulated miRNAs. miR-1290 and miR4428 were commonly up-regulated in the pons and cerebellum of MSA patients. The candidate target genes of the top 5 up-regulated and down-regulated miRNAs in MSA were respectively identified using miRmap web-based open source software.
Among the 50 identified candidate targets for each of the 5 miRNAs, those showing high matching scores and overlapping candidates were considered possible key targets at sites of pathology. The selected target genes are listed in Table 4.
3.3. Ontology analysis of predicted target genes for disease-specific miRNAs From the list of the target candidate genes, the top 50 candidates for each miRNA were annotated and a total of 250 candidates for 5 miRNAs were nominated as targets for ontology analysis. We subjected the 250 genes differentially expressed in MSA to MetaCore, functional network and ontology analysis. As shown in Table 5A, B, the biological processes altered by these target genes in the pons in MSA were found to be related to biosynthesis for both up-regulated and down-regulated miRNAs. In the cerebellum in MSA, the processes of metabolism and axonogenesis were found to be altered for up-regulated miRNAs, and regulation of phospholipase C activity was found to be altered for down-regulated miRNAs (Table 5C, D).
4. Discussion In the present study, we demonstrated that miRNAs extracted from FFPE samples of postmortem brain tissue from patients with MSA and normal controls were relatively well preserved; 26 of 50 samples (52%), for which the longest storage period was 14 years, provided RNA of sufficient quality. The success rate in the present study was almost equal to that in our previous study (46.2%) [31], in which we evaluated FFPE samples from the motor cortex of patients with ALS and normal controls. Thus, miRNAs appear to be relatively stable in FFPE samples, even those from postmortem specimens. However, the success rate in normal controls (32%) was lower than that in
MSA (72%). There was no difference in the postmortem interval or fixation time between MSA and controls. By contrast, the storage period tend to be longer in controls (mean 89.1 months) than MSA (mean 55.8 months), although the difference was not significant (p = 0.07). Longer storage in paraffin may influence the quality of miRNA. Another possibility is that neurodegenerative disorders are linked to progressive loss of neurons and reaction of glial cells (astrocyte and microglia); hence, even if the same area has been selected between affected individuals and normal controls, the composition of the sample could not be similar in term of neuronal and non-neuronal elements [14]. Ubhi et al. [28] investigated miRNA profiles in cases of MSA (n = 3) in comparison with controls (n = 4) using frozen samples of frontal cortex; 214 miRNAs were upregulated and none were down-regulated in comparison with controls. In the present study, 5 miRNAs were up-regulated and 33 were down-regulated in the pons and 5 miRNAs were up-regulated and 18 were down-regulated in the cerebellum of patients with MSA relative to controls. None of the miRNAs found to be up-regulated in our study were up-regulated in the study of Ubhi et al. [28]. On the other hand, 6 of 33 miRNAs down-regulated in the pons as well as 5 of 18 miRNAs down-regulated in the cerebellum in our study were up-regulated in the study of Ubhi et al. [28]. The discrepancy of the results between our study and that of Ubhi et al. may have been attributable to the difference in the samples employed (frozen vs FFPE) or the brain area examined (frontal cortex vs pons and cerebellum). Further studies using larger patient cohorts will be necessary to clarify whether such changes in miRNA expression are global and present in all brain areas, including both affected and unaffected regions. It is noteworthy that in our study 2 miRNAs (miR-1290 and miR-4428) were up-
regulated and 7 miRNAs (miR-129-2-3p, miR-1233-3p, miR-3663-5p, miR-4739, miR4440, miR-129-5p and miR-138-5p) were down-regulated in both the pons and the cerebellum. These findings suggest that pathological process in the pons is similar to that in the cerebellum in MSA. Most of the functions and target genes of these miRNAs in the brain are unknown. miR-1290 might play important roles in neuronal differentiation [35,37]. miR-129-5p might regulate the FMR1 gene, which is silenced in fragile X syndrome, an inheritable neuropsychological disease [38]. miR-138-5p participates in the regulation of oligodendrocyte differentiation and myelin maintenance, as well as in the pathogenesis of demyelination-related diseases such as multiple sclerosis and leukodystrophy [20]. It is important to note that MSA is characterized pathologically by the accumulation of abnormal -synuclein in oligodendrocytes and secondary demyelination [27]. Recently, Lee et al. [18] examined the expression of miRNA in cases of MSA (n = 4) and controls (n = 4) using frozen samples of cerebellum; 9 miRNAs (miR-129-2-3p, miR-129-5p, miR-337-3p, miR-380, miR-433, miR-132-3p, miR-410, miR-206 and miR-409-5p) were down-regulated and 2 (miR-202 and miR-199a-5p) were upregulated in comparison with controls. Three miRNAs (miR-129-2-3p, miR-129-5p and miR-132-3p) found to be down-regulated in the cerebellum in our study were also down-regulated in the study of Lee et al. [18]. In addition, 2 miRNAs (miR-129-2-3p and miR-129-5p) found to be down-regulated in the pons in our study were also downregulated in the study of Lee et al. [18]. Particularly, miR-129-2-3p and miR-129-5p were down-regulated in both the pons and the cerebellum. miR-129-2-3p is one of the metabolic stress-induced miRNAs [16]. miR-129-5p might play roles in the regulation of autophagy in atherosclerosis [11] and cancer [21,34]. miR-132-3p is one of the well-
studied brain-specific miRNAs, which plays a pivotal role in synaptogenesis, synaptic plasticity and structural remodeling [36]. Furthermore, miR-132-3p is known to be down-regulated in the frontal and temporal cortex, hippocampus and cerebellum in patients with Alzheimer’s disease [14]. Interestingly, miR-132-3p is also deregulated in the brain of patients with Parkinson’s [3] and Huntington’s diseases [15]. miR-132-3p is also down-regulated in the brainstem area in -synuclein-transgenic mouse [12]. These findings suggest that miR-132-3p shares common pathomechanisms in certain neurodegenerative disorders. In conclusion, archived FFPE postmortem samples from affected brain regions can be a valuable source for miRNA profiling in neurodegenerative disorders. Targeting neuroprotective pathways controlled by miRNAs may represent a therapeutic strategy for the treatment of neurodegenerative disorders.
Ackonwledgements This work was supported by JSPS KAKENHI Grant Numbers 16K15473 (K.W.) and 26430049 (F.M.), the Collaborative Research Project (2015-2508) of the Brain Research Institute, Niigata University (F.M.), a Grant from Japan Science and Technology Program (AS2314204F) (H.S., K.W.) and the Research Committee for Ataxic Disease (H.S., K.W.) from the Ministry of Health, Labour and Welfare, Japan. The authors wish to express their gratitude to M. Nakata, A. Ono and Y. Hama for technical assistance.
References 1. C. Barbato, F. Ruberti, C. Cogoni, Searching for MIND: microRNAs in neurodegenerative diseases, J. Biomed. Biotechnol. 2009 (2009) 871313. 2. Z. Bing, S.R. Master, J.W. Tobias, D.A. Baldwin, X.W. Xu, J.E. Tomaszewski, MicroRNA expression profiles of seminoma from paraffin-embedded formalin-fixed tissue, Virchows Arch. 461 (2012) 663-668. 3. C.E. Briggs, Y. Wang, B. Kong, T.U. Woo, L.K. Iyer, K.C. Sonntag, Midbrain dopamine neurons in Parkinson’s disease exhibit a dysregulated miRNA and targetgene network, Brain Res. 1618 (2015) 111-121. 4. G. Bruneteau, T. Simonet, S. Bauché, N. Mandjee, E. Malfatti, E. Girard, M.L. Tanguy, A. Behin, F. Khiami, E. Sariali, C. Hell-Remy, F. Salachas, P.F. Pradat, E. Fournier, L. Lacomblez, J. Koenig, N.B. Romero, B. Fontaine, V. Meininger, L. Schaeffer, D. Hantaï, Muscle histone deacetylase 4 upregulation in amyotrophic lateral sclerosis: potential role in reinnervation ability and disease progression, Brain 136(Pt 8) (2013) 2359-2368. 5. L. Chen, Y. Li, Y. Fu, J. Peng, M.H. Mo, M. Stamatakos, C.B. Teal, R.F. Brem, A. Stojadinovic, M. Grinkemeyer, T.A. McCaffrey, Y.G. Man, S.W. Fu, Role of deregulated microRNAs in breast cancer progression using FFPE tissue, PLoS. One 8 (2013) e54213. 6. B. De Felice, A. Annunziata, G. Fiorentino, M. Borra, E. Biffali, C. Coppola, R. Cotrufo, J. Brettschneider, M.L. Giordana, T. Dalmay, G. Wheeler, R. D’Alessandro,
miR-338-3p is over-expressed in blood, CFS, serum and spinal cord from sporadic amyotrophic lateral sclerosis patients, Neurogenetics 15 (2014) 243-253. 7. A.M. Enciu, B.O. Popescu, A. Gheorghisan-Galateanu, MicroRNAs in brain development and degeneration, Mol. Biol. Rep. 39 (2012) 2243-2252. 8. A. Esquela-Kerscher, F.J. Slack, Oncomirs - microRNAs with a role in cancer, Nat. Rev. Cancer 6 (2006) 259-269. 9. A. Freischmidt, K. Müller, A.C. Ludolph, J.H. Weishaupt, Systemic dysregulation of TDP-43 binding microRNAs in amyotrophic lateral sclerosis, Acta Neuropathol. Commun. 1 (2013) 42. 10. H. Geekiyanage, C. Chan, MicroRNA-137/181c regulates serine palmitoyltransferase and in turn amyloid β, novel targets in sporadic Alzheimer’s disease, J. Neurosci. 31 (2011) 14820-14830. 11. Z. Geng, F. Xu, Y. Zhang, MiR-129-5p-mediated Beclin-1 suppression inhibits endothelial cell autophagy in atherosclerosis, Am. J. Transl. Res. 8 (2016) 18861894. 12. F. Gillardon, M. Mack, W. Rist, C. Schnack, M. Lenter, T. Hildebrandt, B. Hengerer, MicroRNA and proteome expression profiling in early-symptomatic α-synuclein (A30P)-transgenic mice, Proteomics Clin. Appl. 2 (2008) 697-705. 13. S.S. Hébert, B. De Strooper, Alterations of the microRNA network cause neurodegenerative disease, Trends Neurosci. 32 (2009) 199-206. 14. J. Hernandez-Rapp, S. Rainone, S.S. Hébert, MicroRNAs underlying memory deficits in neurodegenerative disorders, Prog. Neuropsychopharmacol. Biol. Psychiatry 2016 Apr 23. doi: 10.1016/j.pnpbp.2016.04.011. [Epub ahead of print] 15. R. Johnson, C. Zuccato, N.D. Belyaev, D.J. Guest, E. Cattaneo, N.J. Buckley, A
microRNA-based gene dysregulation pathway in Huntington’s disease, Neurobiol. Dis. 29 (2008) 438-445. 16. S. Kálmán, K.A. Garbett, A. Vereczkei, R.C. Shelton, Z. Korade, K. Mirnics, Metabolic stress-induced microRNA and mRNA expression profiles of human fibroblasts, Exp. Cell Res. 320 (2014) 343-353. 17. J. Kim, K. Inoue, J. Ishii, W.B. Vanti, S.V. Voronov, E. Murchison, G. Hannon, A. Abeliovich, A microRNA feedback circuit in midbrain dopamine neurons, Science 317 (2007) 1220-1224. 18. S.T. Lee, K. Chu, K.H. Jung, J.J. Ban, W.S. Im, H.Y. Jo, J.H. Park, J.Y. Lim, J.W. Shin, J. Moon, S.K. Lee, M. Kim, J.K. Roh, Altered expression of miR-202 in cerebellum of multiple-system atrophy, Mol. Neurobiol. 51 (2015) 180-186. 19. T.S. Lee, H.W. Jeon, Y.B. Kim, Y.A. Kim, M.A. Kim, S.B. Kang, Aberrant microRNA expression in endometrial carcinoma using formalin-fixed paraffinembedded (FFPE) tissues, PLoS. One 8 (2013) e81421. 20. J.S. Li, Z.X. Yao, MicroRNAs: novel regulators of oligodendrocyte differentiation and potential therapeutic targets in demyelination-related diseases, Mol. Neurobiol. 45 (2012) 200-212. 21. J. Luo, J. Chen, L. He, mir-129-5p attenuates irradiation-induced autophagy and decreases radioresistance of breast cancer cells by targeting HMGB1, Med. Sci. Monit. 21 (2015) 4122-4129. 22. L. Ma, R.A. Weinberg, Micromanagers of malignancy: role of microRNAs in regulating metastasis, Trends Genet. 24 (2008) 448-456. 23. P.T. Nelson, W.X. Wang, B.W. Rajeev, MicroRNAs (miRNAs) in neurodegenerative diseases, Brain Pathol. 18 (2008) 130-138.
24. S. Osawa, Y. Shimada, S. Sekine, T. Okumura, T. Nagata, J. Fukuoka, K. Tsukada, MicroRNA profiling of gastric cancer patients from formalin-fixed paraffinembedded samples, Oncol. Lett. 2 (2011) 613-619. 25. A.N. Packer, Y. Xing, S.Q. Harper, L. Jones, B.L. Davidson, The bifunctional microRNA miR-9/miR-9* regulates REST and CoREST and is downregulated in Huntington’s disease, J. Neurosci. 28 (2008) 14341-14346. 26. M. Shioya, S. Obayashi, H. Tabunoki, K. Arima, Y. Saito, T. Ishida, J. Satoh, Aberrant microRNA expression in the brains of neurodegenerative diseases: miR29a decreased in Alzheimer disease brains targets neurone navigator 3, Neuropathol. Appl. Neurobiol. 36 (2010) 320-330. 27. N. Stefanova, G.K. Wenning, Review: Multiple system atrophy: emerging targets for interventional therapies, Neuropathol. Appl. Neurobiol. 42 (2016) 20-32. 28. K. Ubhi, E. Rockenstein, C. Kragh, C. Inglis, B. Spencer, S. Michael, M. Mante, A. Adame, D. Galasko, E. Masliah, Widespread microRNA dysregulation in multiple system atrophy - disease-related alteration in miR-96, Eur. J. Neurosci. 39 (2014) 1026-1041. 29. A. Vallelunga, M. Ragusa, S. Di Mauro, T. Iannitti, M. Pilleri, R. Biundo, L. Weis, C. Di Pietro, A. De Iuliis, A. Nicoletti, M. Zappia, M. Purrello, A. Antonini, Identification of circulating microRNAs for the differential diagnosis of Parkinson’s disease and multiple system atrophy, Front. Cell Neurosci. 8 (2014) 156. 30. C.E. Vejnar, E.M. Zdobnov, miRmap: Comprehensive prediction of microRNA target repression strength, Nucleic Acids Res. 40 (2012) 11673-11683. 31. K. Wakabayashi, F. Mori, A. Kakita, H. Takahashi, J. Utsumi, H. Sasaki, Analysis of microRNA from archived formalin-fixed paraffin-embedded specimens of
amyotrophic lateral sclerosis, Acta Neuropathol. Commun. 2 (2014) 173. 32. W.X. Wang, Q. Huang, Y. Hu, A.J. Stromberg, P.T. Nelson, Patterns of microRNA expression in normal and early Alzheimer’s disease human temporal cortex: white matter versus gray matter, Acta Neuropathol. 121 (2011) 193-205. 33. Y. Xi, G. Nakajima, E. Gavin, C.G. Morris, K. Kudo, K. Hayashi, J. Ju, Systematic analysis of microRNA expression of RNA extracted from fresh frozen and formalinfixed paraffin-embedded samples, RNA 13 (2007) 1668-1674. 34. W. Xiao, B. Dai, Y. Zhu, D. Ye, Norcantharidin induces autophagy-related prostate cancer cell death through Beclin-1 upregulation by miR-129-5p suppression, Tumour Biol. 2015 Dec 5. [Epub ahead of print] 35. S.V. Yelamanchili, B. Morsey, E.B. Harrison, D.A. Rennard, K. Emanuel, I. Thapa, D.R. Bastola, H.S. Fox, The evolutionary young miR-1290 favors mitotic exit and differentiation of human neural progenitors through altering the cell cycle proteins, Cell Death Dis. 5 (2014) e982. 36. A. Yoshimura, T. Numakawa, H. Odaka, N. Adachi, Y. Tamai, H. Kunugi, Negative regulation of microRNA-132 in expression of synaptic proteins in neuronal differentiation of embryonic neural stem cells, Neurochem. Int. 97 (2016) 26-33. 37. H. Zhuang, R. Zhang, S. Zhang, Q. Shu, D. Zhang, G. Xu, Altered expression of microRNAs in the neuronal differentiation of human Wharton’s Jelly mesenchymal stem cells, Neurosci. Lett. 600 (2015) 69-74. 38. S. Zongaro, R. Hukema, S. D’Antoni, L. Davidovic, P. Barbry, M.V. Catania, R. Willemsen, B. Mari, B. Bardoni, The 3’ UTR of FMR1 mRNA is a target of miR101, miR-129-5p and miR-221: implications for the molecular pathology of FXTAS at the synapse, Hum. Mol. Genet. 22 (2013) 1971-1982.
39. Table 1 Summary of formalin-fixed paraffin-embedded samples used for RNA isolation.
Cerebellu Disease Cas
Pathologica
e
l
No.
diagnosis
Fixatio
Storage
Pons m
Postmorte Age/gende
duration
n
period
m interval r
(months
time (weeks)
RNA
microRN
yield
qualit
A
(ng)
y
analysis
(months
(hours) )
RNA
RNA
microRN
qualit
A
y
analysis
RNA yield
)
(ng)
1
MSA-C
57/F
18
20
4
101
1326
S
Yes
1940
S
Yes
2
MSA-C
71/F
12
13
4
92
1137
S
Yes
2250
S
Yes
3
MSA-P
66/M
108
2
4
85
1031
S
Yes
1470
S
Yes
4
MSA-P
61/M
144
3
4
8
259
I
No
504
S
Yes
5
MSA-C
66/M
96
3
4
32
NA
NA
7144
S
Yes
6
MSA-P
64/F
96
4
4
12
2970
S
Yes
5630
S
Yes
7
MSA-P
62/F
204
4.5
4
36
280
I
No
920
S
Yes
8
MSA-C
78/M
84
3
4
36
2840
S
Yes
656
S
Yes
9
MSA-C
82/M
80
2.5
4
48
970
S
Yes
9540
S
Yes
10
MSA-P
67/F
104
2.5
4
48
780
S
Yes
7550
S
Yes
11
MSA-P
75/M
96
3
4
72
1260
I
No
8690
I
No
12
MSA-P
65/F
84
4
4
72
4140
I
No
4250
I
No
13
MSA-P
68/M
36
6
4
84
560
I
No
4780
S
Yes
14
Control
37/M
12
3
101
37
I
No
NA
NA
15
Control
71/F
10
3
86
735
S
Yes
3740
S
Yes
16
Control
84/M
10
4
64
617
S
Yes
1280
S
Yes
17
Control
67/F
4
4
38
4610
S
Yes
1640
S
Yes
18
Control
85/F
1
4
5
49
I
No
668
I
No
19
Control
60/F
2
4
12
290
I
No
11620
I
No
20
Control
76/M
3
4
60
2290
I
No
11650
I
No
21
Control
51/M
6
4
72
230
I
No
8530
I
No
22
Control
82/M
6
4
168
1090
I
No
220
I
No
23
Control
55/F
4
4
108
90
I
No
9720
I
No
24
Control
79/F
2.5
4
168
3540
S
Yes
12560
I
No
25
Control
80/M
3
4
144
490
I
No
6890
S
Yes
26
Control
82/M
2
4
132
2690
I
No
4150
I
No
MSA-C, multiple system atrophy-cerebellar; MSA-P, multiple system atrophy-parkinsonism; S, sufficient; I, insufficient; NA, not available.
Table 2 Pathological finding in cases of MSA for microRNA study.
Case
Pathological
Neuronal loss Neuronal loss
No.
diagnosis
in pons
in cerebellum
1
MSA-C
1
1
2
MSA-C
1
1
3
MSA-P
2
2
4
MSA-P
2
2
5
MSA-C
3
3
6
MSA-P
2
2
7
MSA-P
3
3
8
MSA-C
3
3
9
MSA-C
3
3
10
MSA-P
3
3
13
MSA-P
1
1
Neuronal loss: 1 mild, 2 moderate, 3 severe.
Table 3 MicroRNAs up- and down-regulated in the pons and cerebellum in MSA.
Pons
Up-regulated
Down-regulated
Cerebellum
miRNA name
Ratio
miRNA name
Ratio
miR-1290
1.62
miR-4428
1.92
miR-21-5p
1.59
miR-4732-5p
1.79
miR-30b-5p
1.58
miR-1290
1.73
miR-4428
1.52
miR-3619-3p
1.54
miR-23a-3p
1.51
miR-4725-3p
1.5
miR-128-3p
0.67
miR-4739
0.67
miR-371b-3p
0.66
miR-4726-3p
0.66
miR-3928-3p
0.66
miR-1228-3p
0.66
miR-1915-3p
0.66
miR-346
0.65
miR-129-2-3p
0.65
miR-134-5p
0.64
miR-1203
0.65
miR-1233-3p
0.64
miR-584-5p
0.65
miR-484
0.63
miR-1910-5p
0.65
miR-138-5p
0.62
miR-675-5p
0.64
miR-132-3p
0.61
miR-149-5p
0.64
miR-3663-5p
0.61
miR-1233-3p
0.64
miR-4440
0.61
miR-3173-5p
0.63
miR-3184-5p
0.6
miR-1539
0.63
miR-557
0.59
miR-513a-5p
0.62
miR-3907
0.56
miR-3663-5p
0.62
miR-129-5p
0.46
miR-4723-3p
0.61
miR-219a-2-3p
0.43
miR-4739
0.61
miR-129-1-3p
0.37
miR-4440
0.61
miR-129-2-3p
0.33
miR-1909-5p
0.6
miR-129-5p
0.6
miR-330-5p
0.59
miR-572
0.59
miR-4632-3p
0.57
miR-940
0.56
miR-1231
0.55
miR-124-3p
0.53
miR-34a-5p
0.51
miR-210-3p
0.51
miR-4687-5p
0.51
miR-127-3p
0.5
miR-138-5p
0.48
miR-379-5p
0.46
miR-219a-5p
0.36
Table 4 Predicted target genes for MSA-specific miRNAs identified by miRmap web-based open
source software.
Gene
Gene
symbol
ID
Gene name
bone morphogenetic protein receptor, For up-regulated miRNA
BMPR2
659 type II suppressor of glucose, autophagy
in pons
SOGA1
140710 associated 1
PRLR
5618
prolactin receptor G protein-coupled receptor, family C, group 5,
GPRC5A
9052 member A cAMP responsive element binding
CREB5
9586 protein 5
For
down-regulated
glycosyltransferase GXYLT1
miRNA in pons
8
domain
283464 containing 3
ELMOD2
255520
NFAT5
10725
ELMO/CED-12 domain containing 2 nuclear factor of activated T-cells 5, tonicityresponsive
ZNF207
7756
SOGA3
387104
zinc finger protein 207 suppressor of glucose, autophagy associated, family member 3 protein phosphatase 1, regulatory (inhibitor)
For up-regulated miRNA
PPP1R12B 4660 subunit 12B
in cerebellum
SSR1
6745
signal sequence receptor, alpha LIM domain containing preferred translocation partner in
LPP
4026 lipoma RAB3B, member RAS oncogene
RAB3B
5865 family
For
ZNF704
619279
zinc finger protein 704
CDK6
1021
cyclin-dependent kinase 6
CENPP
401541
centromere protein P
SIM1
6492
single-minded homolog 1 (Drosophila)
SESTD1
228071
SEC14 and spectrin domains 1
STXBP4
252983
syntaxin binding protein 4
down-regulated
miRNA in cerebellum
Table 5 Ontology analysis of predicted target genes for disease-specific miRNAs in MSA.
(A)
GO Processes of target genes for up-regulated miRNAs in pons
p-value
FDR
1
regulation of cellular macromolecule biosynthetic process
6.93E-13
2.14E-09
2
regulation of macromolecule biosynthetic process
1.05E-12
2.14E-09
3
transcription, DNA-templated
2.93E-12
3.09E-09
4
regulation of transcription, DNA-templated
3.52E-12
3.09E-09
5
regulation of RNA metabolic process
3.80E-12
3.09E-09
6
regulation of RNA biosynthetic process
6.56E-12
3.82E-09
7
regulation of cellular process
6.58E-12
3.82E-09
8
biological regulation
1.22E-11
6.11E-09
9
regulation of nucleobase-containing compound metabolic process 1.35E-11
6.11E-09
10
RNA biosynthetic process
6.17E-09
(B)
1.52E-11
GO Processes of target genes for down-regulated miRNAs in pons p-value
FDR
1
organic substance biosynthetic process
1.14E-09
5.55E-06
2
biosynthetic process
3.19E-09
7.16E-06
3
cellular biosynthetic process
4.40E-09
7.16E-06
4
regulation of multicellular organismal development
1.10E-07
1.27E-04
5
nervous system development
1.84E-07
1.27E-04
6
macromolecule biosynthetic process
1.86E-07
1.27E-04
7
regulation of cell development
2.08E-07
1.27E-04
8
cellular macromolecule biosynthetic process
2.09E-07
1.27E-04
9
rhythmic process
3.11E-07
1.58E-04
10
cell-cell signaling
3.30E-07
1.58E-04
p-value
FDR
GO Processes of target genes for up-regulated miRNAs in (C) cerebellum 1
positive regulation of biological process
3.58E-10
1.25E-06
2
regulation of cellular metabolic process
1.28E-09
2.24E-06
3
central nervous system projection neuron axonogenesis
3.50E-09
3.13E-06
4
regulation of metabolic process
3.57E-09
3.13E-06
5
regulation of primary metabolic process
1.53E-08
1.07E-05
6
regulation of nitrogen compound metabolic process
1.99E-08
1.14E-05
7
biological regulation
2.47E-08
1.14E-05
8
cell morphogenesis involved in differentiation
3.35E-08
1.14E-05
9
positive regulation of cellular process
3.53E-08
1.14E-05
10
central nervous system neuron axonogenesis
3.53E-08
1.14E-05
p-value
FDR
GO Processes of target genes for down-regulated miRNAs in (D) cerebellum 1
positive regulation of phospholipase C activity
1.19E-12
4.53E-09
2
regulation of phospholipase C activity
1.92E-12
4.53E-09
3
positive regulation of phospholipase activity
3.20E-12
5.04E-09
4
positive regulation of lipase activity
1.81E-11
1.71E-08
5
regulation of phospholipase activity
1.81E-11
1.71E-08
6
establishment of localization
3.61E-11
2.84E-08
7
negative regulation of glucose transport
4.93E-11
3.33E-08
8
regulation of smooth muscle cell proliferation
6.23E-11
3.68E-08
9
localization
1.02E-10
5.32E-08
10
regulation of transport
1.32E-10
6.21E-08
p-value, the probability of a random intersection of 2 different gene/protein/compound sets; FDR, false discovery rate.
40.