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Article

MiRNA Expression in Patients with Gaucher Disease Treated with Enzyme Replacement Therapy

1
Clinical Department of Metabolic Diseases and Diabetology, University Hospital in Krakow, 30-688 Kraków, Poland
2
European Reference Network for Hereditary Metabolic Disorders (MetabERN), 31-501 Kraków, Poland
3
Department of Clinical Biochemistry, Jagiellonian University Medical College, 30-688 Kraków, Poland
4
Department of Metabolic Diseases and Diabetology, Jagiellonian University Medical College, 30-688 Kraków, Poland
*
Author to whom correspondence should be addressed.
Submission received: 4 November 2020 / Revised: 17 December 2020 / Accepted: 18 December 2020 / Published: 22 December 2020
(This article belongs to the Section Medical Research)

Abstract

:
Aims: The aim of the work was to establish potential biomarkers or drug targets by analysing changes in miRNA concentration among patients with Gaucher disease (GD) compared to in healthy subjects. Methods: This study was an observational, cross-sectional analysis of 30 adult participants: 10 controls and 20 adults with GD type 1. Patients with GD type 1 were treated with enzyme replacement therapy (ERT) for at least two years. The control group was composed of healthy volunteers, unrelated to the patients, adjusted with age, sex and body mass index (BMI). The miRNA alteration between these groups was examined. After obtaining preliminary results on a group of six GD patients by the high-output method (TaqMan low-density array (TLDA)), potential miRNAs were selected for confirming the results by using the qRT-PCR method. With Diane Tools, we analysed miRNAs of which differential expression is most significant and their potential role in GD pathophysiology. We also determined the essential pathways these miRNAs are involved in. Results: 266 dysregulated miRNAs were found among 753 tested. Seventy-eight miRNAs were downregulated, and 188 were upregulated. Thirty miRNAs were significantly altered; all of them were upregulated. The analysis of pathways regulated by the selected miRNAs showed an effect on bone development, inflammation or regulation of axonal transmission in association with Parkinson’s disease. Conclusions: We revealed few miRNAs, like miR-26-5p, which are highly altered and fit the GD pathophysiological model, might be considered as novel biomarkers of disease progression but need further evaluation.

1. Introduction

Among lysosomal storage diseases, Gaucher disease (GD) is one of the most common. It is estimated that around 1 per 57,000 newborns is affected worldwide [1]. The disease’s phenotype is associated with a mutation in the lysosomal glucosylceramidase (GBA) gene; however, it differs remarkably even in patients with the same type of mutation [2,3,4]. Three types of GD are recognized upon central nervous system (CNS) involvement: non-neuronopathic (GD type 1: OMIM #230800), acute neuronopathic (GD type 2: OMIM #230900) and chronic neuronopathic (GD type 3: OMIM #231000). The most common form of GD, covering approximately 94% of cases, is type 1, which can manifest itself at any age [5]. It is characterised by haematological changes such as thrombocytopenia, anaemia and highly pronounced splenomegaly. Sometimes, hepatomegaly may coexist, and changes occurring in the skeletal system are also important [5]. Type 2 with a very severe course, regardless of the treatment attempts, leads to death around two years of age, while type 3, in addition to organ changes, is also associated with the involvement of the central nervous system, characterised by the presence of drug-resistant epilepsy; however, in this case, patients live to adulthood [5].
GD patients are also more susceptible to some diseases like heamatological neoplasms, Parkinson’s disease (PD) or central nervous involvement than the normal population [6].
One of the suspected reasons for that variability is an epigenetic mechanism that can modulate the course of the illness [7]. That is carried by, among others, microRNAs (miRNA), which are small noncoding RNA molecules found in almost all life forms and are responsible for RNA silencing and post-transcriptional regulation of gene expression. Therefore, to delve into it, we marked the expression of 753 miRNAs in GD patients, compared it to that in the control, healthy group, and for those miRNAs that were altered, we evaluated their potential role, compared with available data and analysed molecular pathways that they are involved in. All of the tested miRNAs are shown in the Appendix A (Table A1).
Currently, the possible role of organ-specific miRNAs in predicting the development of metabolic diseases such as diabetes and its complications or the development of complications in the course of lysosomal storage diseases (LSDs) is widely discussed [8,9]. Due to the stability of miRNAs in body fluids and distribution in specific tissues, it may turn these particles into a desirable biomarker of certain diseases [9]. Siebert, in his work on fibroblast cell lines from GD patients, showed that some miRNAs had a strong effect on the regulation of β-glucocerebrosidase (GCase) activity [10]. Two miRNAs (miR-195-5p and miR-16-5p) strongly stimulated GBA1 gene expansion, while miR-127-5p, miR-19a-5p and miR-1262 inhibited the expression of SCARB2, a membrane receptor regulating GCase activity and availability [10]. MicroRNAs were shown to downregulate GBA and GBAP1 (miR-22-3p) [11]. Some miRNAs like miR-let7b, miR-29b or miR-142 were associated with proinflammatory events [12]. Recently, Watson et al. described an unsuccessful ablation of miR-155, which was identified in knocked-down gba1 zebrafish (in vivo GD model) as a proinflammatory master regulator [13]. However, the work on the role of miRNAs in GD only focused on the modifying role of this epigenetic mechanism in GBA gene expression [10]. It is worth remembering that most of these works concerned in vivo and in vitro observation, and we identified only one human observation [10].
Nevertheless, the big picture is still missing. Therefore, our work aimed to investigate changes in miRNAs concentration in patients with GD who were treated with enzyme replacement therapy (ERT) and to try finding new diagnostic markers or potential targets for treatment.

2. Material and Methods

This study was an observational, cross-sectional analysis of 30 adult participants: 20 adults with GD type 1 and 10 controls. First, samples from 8 random persons (4 from the GD group and 4 from the control one) were used for the TaqMan low-density array (TLDA), a high-throughput method for identifying alterations in miRNAs expression levels. Based on these preliminary results, potential miRNAs were selected for a confirmation on a whole group (30 persons) using the more precise qRT-PCR method. Patients with GD type 1 were treated in the Metabolic Diseases and Diabetes Department in the Jagiellonian University Hospital in Krakow, part of EU reference centres—MetabERN. The study included adult people with GD, treated in our centre, who agreed to participate in the study and signed up an informed consent. GD diagnosis was established on the positive genetic or/and enzymatic tests and, in two cases, additional histopathological examination. All these patients underwent ERT for at least two years. The exclusion factors for the analysis were as follows: neoplastic diseases, PD, autoimmune diseases, pregnancy, infection, fever or lack of patient’s consent to participate in the study. The control group was composed of healthy volunteers, not related to the patients, adjusted with age, sex and BMI.
All the participants underwent a detailed physical examination (age, weight, height, BMI, age and any disease history). Fasting venous blood was used for molecular biology tests. Molecular tests were performed at the Clinical Biochemistry Department of the Jagiellonian University. The clinical characteristic of groups is presented in Table 1.

3. The miRNA Analysis

3.1. RNA Isolation

The purification of total RNA from human whole blood was performed using the PAXgene Blood RNA Kit (Qiagen, Germantown, MD, USA), following the manufacturer’s protocol. The RNA quality was analysed using the Tapestation 2200 instrument (Agilent Technologies, Santa Clara, CA, USA) and quantified by spectrophotometry on the NanoDrop (Thermo Fisher Scientific, Wilmington, DE, USA).

3.2. TLDA

The initial expression analysis of human microRNAs (753 unique assays) was performed by a high-throughput method using the TLDAs Panel v3.0 (Applied Biosystems, Foster City, CA, USA), as described by the manufacturer. Total RNA enriched with microRNAs was reverse-transcribed using stem-loop primers. In order to detect low abundant microRNAs, a preamplification step was performed. The preamplified product was loaded into a TLDA, and amplification signal detection was performed in the 7900 FAST real-time thermal cycler (ABI) (Thermo Fisher Scientific, Wilmington, DE, USA). Data were exported to the DataAssist software version 3.01 (Life Technologies, now Thermo Fisher Scientific, Waltham, MA, USA) and normalised using the small-nucleolar RNA U6snRNA. Mean relative quantity (RQ) was calculated, and microRNAs differentially expressed between groups were defined as those with a fold change of >1.5 and a p-value of <0.05. The differentially expressed miRNAs based on the TLDAs results were verified using microRNA assays (TaqMan Array Human MicroRNA Panel v3.0).

3.3. qRT-PCR

Subsequently, to validate the microarray experiment, qRT-PCR was conducted on 20 patients with GD type 1 and 10 volunteers from the control group. Total RNA was reverse-transcribed using a specific stem-loop RT primer for each microRNA and the MultiScribeTM reverse transcriptase (Thermo Fisher Scientific, Wilmington, DE, USA). Then, diluted retro-transcription reaction (1:15) was mixed with the Universal PCR Master Mix, No AmpErase R UNG (2X), in the presence of individual PCR microRNAs. The PCR reactions were done in a GeneAmp R PCR System 9700 (Applied Biosystems, Foster City, CA, USA), using the following program: 95 °C for 10 min, 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The relative expression of microRNAs was measured by qRT-PCR using the comparative Ct (∆∆CT) method. A two-tailed Student’s t-test comparing the 2(–ΔCt) values of the two groups was performed, and the Benjamini–Hochberg-adjusted p-value was calculated. The U6 snoRNA was used as an internal control for data normalisation.

3.4. Pathway Enrichment

MicroRNA targets were predicted using a microRNA target prediction program: DIANA-microT (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=microT_CDS/index). Biological pathways of predicted target-genes microRNAs were identified using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis (http://www.genome.jp/kegg/pathway.html) and mirPath v.3 (http://snf-515788.vm.okeanos.grnet.gr/).
Briefly, the DIANA-mirPath utilised predicted miRNA targets provided by the miRNA-gene interactions derived from experimentally supported DIANA-TarBase v.7.0 (Department of Electrical and Computer Engineering, 382 21 Volos, Greece) or microT-CDS target prediction, if there were no hits for DIANA-TarBase. The KEGG pathways union was used to identify the main pathways targeted by predicted genes (discovered miRNA targets).

3.5. Statistical Analysis of Patients’ Baseline Characteristics

For the analysis of the characteristics of the study groups, IBM SPSS Statistic Software was used. The Shapiro–Wilk test was performed to check the normality. Differences between groups were assessed using the T-test or U-test for normally and non-normally distributed continuous variables, respectively, and by the chi-square or Fischer’s exact test for categorical variables. Continuous variables are presented as mean ± SD or median (interquartile range). Statistical significance was set at p < 0.05.

4. Results

The analysed groups: control and GD patients were comparable in gender distribution, age and anthropometric parameters. To unify patients’ group as much as possible, all of the patients with GD type 1 were treated with ERT.
Using the TLDA method, we were able to perform an accurate quantitation of 753 microRNAs in the study group. U6 comparison data were used for normalization. Alterations in miRNAs expression were determined in 266 cases, among which 78 were downregulated and 188 were upregulated. Eventually, among all tested miRNAs, we identified 30 miRNAs, of which the expression significantly differed between GD patients and our control data (Table 2). Interestingly, all these miRNAs were upregulated in relation to the control group.
The most prominent alteration in the GD group included miR-26b-5p, miR-31-5p, miR-29a-3p, miR-454-3p, miR-660-5p and miR-148a-3p. The miRNAs were mapped for molecular pathways to identify those, which could be specifically modified in GD patients (Table 3). Among pathways, we chose the most noticeable ones according to the p-value, the proportion of miRNAs to controlled genes and actual knowledge about the pathophysiology of the lysosomal storage disease.
The analysis using a heatmap (Figure A1) showed a strong relationship between the TGF-beta signal transmission pathway and miR-106a-5p. In addition, a significant association was found between pathways of fatty acid synthesis and metabolism, with miR-16-5p and miR-195-5p. Interestingly, these miRNAs played also a significant role in the regulation of the Akt-signalling pathway, focal adhesion or lysine degradation pathways. Figure 1 Additionally, we noted an important impact of differentially expressed miRNA on a very complex pathway of extracellular matrix (ECM)–receptor interaction, namely miR-29a-3p or miR-let-7g-5p.

5. Discussion

5.1. Comparison to Available Data

The biological purpose of miRNAs is related to their conservative function, common for many lifeforms, to control groups of genes by muting them when needed. They influence biochemical pathways on many levels to create a complex and tangled network of dependencies and controlling apparatus.
Nowadays, research to recognise miRNA in-depth role in the ability to regulate genes expression is blooming. As for GD, only a few miRNAs analyses have been described so far. In 2014, Siebert reported data for two miRNAs: 195-5p and 16-5p concentration changes in GD and linked them to the regulation (increase) of GCase activity [10]. Our data showed the upregulation of these two. In the presence of GCase insufficiency, the results suggested a secondary hyperconcentration of regulators and may confirm in vivo the importance of miR-195-5p and miR-16-5p in GCase secretion.

5.2. GD and Malignancies

miR-195-5p was also recently found to be downregulated in a broad spectrum of solid cancers. It was described as a cancer suppressor [14], which mediates its effect by complex and still vogue pathways. Similarly, miR-16-5p was described as a protective factor in breast carcinoma [15], and again, we showed significant upregulation of this miRNA.
Highly elevated levels of miR-195-5p and 16-5p may provide a protective role in GD in terms of solid tumours [16,17,18]. Until now, GD patients were found to be more susceptible primarily for haematologic neoplasms [19]. However, the data of breast cancer in GD are inconsistent. In 2015, European Registry did not report any incidence of breast cancer; neither did Shiran in 1993 [19,20]. In 2009, Taddei reported nine cases of breast cancer, with a risk ratio of 1.84 [21]. Therefore, these observations require further studies.
For miR-148b-3p, the connection between high concentration and poor survival in breast cancer was established [22]. Our data showed a 27.5-fold increase in this miRNA in the study group, but again, there are no available studies that investigated the mentioned issue.
Few papers described an increased number of cases with hepatocellular carcinoma (HCC) [23]. Zhao in 2017 revealed that the expression of miR-31-5p was significantly upregulated in HCC tissues [24]. This information is consistent with our analysis showing highly altered miR-31-5p that was more than 50 times higher than in the comparison group. In our tested groups, there was no HCC incidence. The question remains: is miR-31-5p just a marker for HCC or does it present the properties for the highly sensitive biomarker of the liver lesion which could reveal the destruction of hepatic cells, even when classical tests such as alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are within a normal range?
All the patients in the study group were on ERT for at least two years. This treatment could influence the concentrations of many miRNAs and change the course of the disease, including complication occurrence. However, the data from European Registry [20] did not reveal differences in cancer incidence between patients ever treated with ERT and those never treated with ERT, but still, the impact of ERT on miRNAs, which are associated with malignancies, is limited as the data are missing. There are also no data on the dependence of the miRNA concentration on the ERT presence.

5.3. Other Highly Upregulated miRNA

The most prominent upregulated miRNA was found to be miR-26b-5p, which was linked with TRPS1 gene, which is responsible for regulating genes involved in the growth of bone and cartilage. Therefore TRPS1 controls chondrocyte proliferation and differentiation. MiR-26b-5p controls also bone morphogenetic protein 2 (BMP-2) that affects osteoblasts and regulates proliferation, differentiation and apoptosis [25]. In patients with GD, bone involvement is one of the main symptoms and consists of distortion of the vertebra and distension of long bones, known as Erlenmeyer flask bone deformity, with infiltration of bone marrow. It has been demonstrated that miR-26b-5p with a 66-fold increase of expression in our study group is a potential biomarker of bone involvement.

5.4. miRNA Pathways Analysis

To put altered miRNAs into the bigger picture, we used the DIANA-mirPath v3 tool for analysing miRNA-predicted targets with KEGG pathways. Nineteen pathways were significantly altered. Those, most noticeable influenced and possibly important in GD pathophysiology are “TGF-beta signalling pathway”, “focal adhesion”, “FoxO signalling pathway”, “axon guidance” and “ECM–receptor interaction”.
The TGF-beta signalling pathway is found in numerous human disorders. As it controls the growth inhibitors, the alteration of its activity is observed in cancers [26], which are more frequent in GD. In addition, TGF-beta stimulates ECM deposition that may lead to fibrosis and scarring [27]. The higher prevalence of liver fibrosis in GD was also shown [28].
The focal adhesion pathway focuses on the regulation of call–matrix interaction. The central point of that pathway—focal adhesion kinase (FAK) was roughly studied in terms of cancer progression [29,30,31]. Likewise, FAK was found to promote proinflammatory gene expression via TNF-α or IL-1β [32].
The FoxO signalling pathway, on the other hand, is involved in oxidation stress and may play an important role in neurodegenerative disease like Parkinson’s [33,34].
The axon guidance pathways involve, among others, miR-20a-5p, 106a-5p, 27b-3p or 182-5p as regulators, which in our analysis showed significant overexpression. Among these, 182-5p was found to interact with cofilin-1 (CFL1) and further axon growth [34]. As axon guidance is responsible for controlling neurons morphogenesis and signalling processes [35], its malfunction may contribute to neuropathy in GD patients.
These data confirmed the previously described role of inflammatory processes and the disrupted pathways of cell migration and interaction with the ECM, that consists of a mixture of structural and macromolecules and serves an important role not only in tissue morphogenesis but also in the regulation of cell and tissue structure and function.

5.5. miRNA and PD

Since the relationship between PD and GD was revealed, many studies were trying to establish the underlying pathophysiology. It was shown that patients with PD had a decreased level of GCase in the brain tissue. Moreover, even carries of the single GBA gene mutation present tendency towards PD [36]. This finding showed that GD patients should be more susceptible to the occurrence of PD, which was confirmed in [36]. Research on the expression of genes related to neuroinflammation and synaptic plasticity are ongoing [12]. MiR-26b and miR-106a-5p acting through HSPA8 [37], as well as miR-7 and miRNA-153, are found to increase the concentration of alpha-synuclein [38], the primary accumulation material in PD. We noted a 14-fold increase of miR-106-5p in our study group, which is consistent with the known data.

6. Conclusions

GD patients are still in need of new biomarkers and therapeutic targets that can help evaluate disease progression or treatment effectiveness. We highlighted some miRNA compounds that showed remarkable alteration in those patients and might be potential biomarkers. However, due to scarce data on GD, these findings need further studies.

Author Contributions

Ł.P., M.S., E.T. and M.K. performed the research. B.S., M.S. and A.P. analysed data. B.K.-W. designed the research study and interpreted the data. Ł.P. and B.K.-W. wrote the paper. B.K.-W. and Ł.P. are the guarantors of this work. All authors have read and agreed to the published version of the manuscript.

Funding

The study was funded by the Jagiellonian University Scientific Grant to B.K.-W. (No. N41/DBS/000069).

Institutional Review Board Statement

All procedures performed in this study, involving human participants, were in accordance with the ethical standards of the Jagiellonian University Bioethical Committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards (KBET 122.6120.90.2015).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Acknowledgments

Several authors of this publication are members of the European Reference Network for Rare Hereditary Metabolic Disorders (MetabERN)—Project ID No 739543.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Figure A1. Heatmap presenting miRNA impact in specific pathways.
Figure A1. Heatmap presenting miRNA impact in specific pathways.
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Table A1. List of all of the tested miRNAs. For 266 altered miRNAs: fold change, p-value and SD data are presented.
Table A1. List of all of the tested miRNAs. For 266 altered miRNAs: fold change, p-value and SD data are presented.
miRNAFold Changep-ValueSD
hsa-miR-152-00047518.13820.00031.956398
hsa-miR-454-00232338.57670.00040.887427
mmu-miR-140-00118732.74310.00220.796695
hsa-miR-532-0015186.73610.00681.858579
hsa-miR-148b-00047111.42680.00840.930063
hsa-miR-17-00230816.90610.00881.105731
hsa-miR-148a-00047032.99180.00891.748115
mmu-miR-93-0010905.70930.00890.81462
hsa-miR-106a-00216917.34870.00951.104199
hsa-miR-20a-00058017.02110.00971.115662
hsa-miR-324-5p-00053926.08590.010.863699
hsa-miR-16-00039124.67790.01021.212765
hsa-miR-660-00151532.83250.01051.537621
hsa-miR-126-00222824.59530.01061.214868
hsa-miR-26b-00040766.90290.0110.894591
hsa-miR-29c-00058722.63990.01110.729712
hsa-miR-140-3p-0022346.08360.01121.477748
hsa-miR-30b-0006024.27520.01561.025196
hsa-miR-106b-00044234.88230.01581.020234
hsa-miR-146b-00109728.66680.01710.745407
mmu-miR-451-00114122.35680.01731.281826
hsa-miR-212-0005154.0140.01911.005979
hsa-miR-31-00227961.57570.02253.805273
hsa-miR-182-00233410.91750.02691.014663
hsa-miR-20b-00101415.1020.02810.968618
hsa-miR-185-00227119.91980.02911.583043
hsa-miR-27a-00040822.61430.03021.593832
hsa-miR-22-0003988.9340.03171.16643
hsa-miR-26a-00040519.1040.03281.213354
mmu-miR-374-5p-00131910.45280.03291.260503
hsa-miR-128a-00221615.28160.0332.046559
hsa-miR-18a-00242240.50590.03671.138078
hsa-miR-210-0005125.18330.03791.053872
hsa-miR-194-00049330.59510.03881.389726
hsa-miR-192-00049131.2440.03890.902125
hsa-miR-191-0022995.57930.04011.600253
hsa-miR-28-00041119.45320.04050.901813
hsa-miR-146a-00046830.8470.04291.382615
hsa-miR-130b-0004565.31970.04331.578003
hsa-miR-184-0004855.54760.04440.8337
hsa-miR-362-0012733.83320.04490.995367
hsa-miR-130a-00045437.30760.04691.578769
hsa-miR-500-0024282.85250.0471.113499
hsa-miR-195-00049426.57320.04871.167158
hsa-miR-199a-3p-00230423.12460.05341.014663
hsa-miR-502-3p-0020833.37670.05480.812816
hsa-miR-155-0026239.72130.05771.37917
hsa-miR-590-5p-001984324.19590.05780.642024
hsa-miR-24-0004029.95020.05811.384917
hsa-miR-374-00056373.63360.05870.699017
hsa-miR-339-3p-00218413.39320.06091.538581
hsa-miR-425-5p-0015163.9570.0611.162153
hsa-miR-27b-0004096.84490.06251.167805
hsa-miR-25-0004037.16210.0631.363864
hsa-miR-29a-00211262.44110.06561.976845
hsa-miR-345-0021867.40860.0661.380509
hsa-miR-183-0022699.28490.06860.889644
hsa-miR-331-0005453.98720.06871.152766
hsa-miR-103-0004396.60690.06991.220862
hsa-miR-101-00225319.05210.07010.592054
hsa-miR-30c-0004193.19920.07231.025552
hsa-miR-133a-0022469.00330.07261.228076
hsa-miR-223-00229516.26440.07721.533045
hsa-miR-744-0023242.67740.07981.38857
hsa-miR-221-0005242.92260.08071.312939
hsa-miR-363-00127125.85160.08461.616082
hsa-miR-376c-00212233.94270.09110.474342
hsa-miR-139-5p-0022897.03360.09131.214447
hsa-miR-132-0004573.84280.0961.014592
hsa-miR-301-00052864.89110.09760.509057
hsa-miR-181c-0004824.7080.11.0808
hsa-let-7g-0022828.76540.10141.107341
hsa-let-7f-0003829.04170.10160.968148
hsa-miR-361-00055418.41770.10292.978323
hsa-miR-21-00039748.01590.1030.939588
hsa-miR-200c-0023003.33120.10511.512568
hsa-miR-186-00228516.60370.11080.786326
hsa-miR-27a#-0024457.37050.11170.965802
hsa-miR-502-0011092.64280.11370.975355
hsa-miR-501-0010472.58670.11851.056212
hsa-miR-222-00227611.3680.11951.742912
hsa-miR-342-5p-0021473.59730.12481.323263
hsa-miR-126#-00045140.99230.1260.935753
hsa-miR-19b-00039691.42270.12610.75054
hsa-miR-19a-000395190.4110.13110.747321
hsa-let-7e-0024064.42220.13341.317315
hsa-miR-28-3p-0024462.63310.13650.962861
hsa-miR-539-00128610.3610.13710.73037
hsa-miR-1233-0027680.18140.13811.095964
hsa-miR-376a-0005656.01980.14590.517776
hsa-miR-629-0024364.94590.14871.014592
hsa-miR-324-3p-0021613.1530.15011.014592
hsa-miR-1255B-0028019.21950.15191.384534
hsa-miR-335-00054657.17380.15360.403013
hsa-miR-142-3p-000464140.54120.16210.664435
hsa-miR-181a-0004803.20780.16211.395227
hsa-miR-1227-0027690.10240.16330.966204
hsa-let-7a-0003773.21890.16921.31003
hsa-let-7d-0022833.25350.17071.318228
hsa-miR-204-00050813.03550.17631.520136
hsa-miR-10a-0003875.0520.18270.691164
hsa-miR-1260-0028960.16450.18411.057165
hsa-miR-628-3p-0024345.06330.18751.168615
hsa-miR-365-0010202.39930.191.761739
hsa-miR-744#-0023254.62790.19092.144439
hsa-miR-1290-0028630.12280.19130.690733
hsa-miR-370-0022755.1380.19670.96788
hsa-miR-520c-3p-0024000.22610.1971.062601
hsa-miR-1238-0029270.06980.20071.014592
hsa-miR-26b#-0024446.09290.20321.40211
rno-miR-7#-00133879.95030.20543.010285
hsa-miR-1282-0028030.15370.20592.198623
hsa-miR-1285-0028226.1230.2061.266722
hsa-miR-1249-0028680.12540.20690.976979
hsa-miR-190b-0022636.60020.2091.199389
hsa-miR-193a-5p-0022812.39050.20921.38934
hsa-miR-151-5P-0026425.97350.21081.25475
hsa-miR-211-0005143.32120.21372.352509
hsa-miR-486-0012780.18040.21631.319325
hsa-miR-505-0020893.07980.22071.078406
U6 snRNA-0019731.61330.22240.81169
hsa-miR-223#-00209813.22030.2251.208402
hsa-miR-1224-3P-0027520.05460.22521.175195
hsa-miR-26a-1#-00244311.41730.22621.014592
hsa-miR-655-0016122.24360.22831.230803
hsa-miR-15b-0003903.02610.22971.222556
hsa-miR-618-0015932.32270.23010.922167
hsa-miR-100-0004375.47360.23190.585037
hsa-miR-1236-0027610.09530.23341.026049
hsa-miR-196b-0022159.10660.23451.099845
hsa-miR-139-3p-0023132.33130.23591.557573
hsa-miR-197-0004970.10180.23611.170318
hsa-miR-425#-0023023.25330.23641.391461
hsa-miR-99a-0004353.97740.24020.919231
hsa-miR-494-0023652.59050.24311.178213
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hsa-miR-633-001574
hsa-miR-634-001576
hsa-miR-635-001578
hsa-miR-637-001581
hsa-miR-638-0015820.746
hsa-miR-639-0015830.4233
hsa-miR-640-001584
hsa-miR-641-001585
hsa-miR-643-001594
hsa-miR-644-001596
hsa-miR-646-001599
hsa-miR-647-001600
hsa-miR-648-001601
hsa-miR-649-001602
hsa-miR-650-001603
hsa-miR-651-001604
hsa-miR-653-002292
hsa-miR-654-3p-002239
hsa-miR-656-001510
hsa-miR-657-001512
hsa-miR-658-001513
hsa-miR-659-001514
hsa-miR-661-0016062.7873
hsa-miR-662-001607
hsa-miR-663B-0028570.1863
hsa-miR-665-002681
hsa-miR-672-002327
hsa-miR-674-002021
hsa-miR-675-002005
hsa-miR-708#-002342
hsa-miR-708-0023410.4321
hsa-miR-7-2#-00231426.3744
hsa-miR-758-0019900.2387
hsa-miR-765-002643
hsa-miR-767-3p-001995
hsa-miR-767-5p-001993
hsa-miR-769-3p-0020032.3061
hsa-miR-770-5p-002002
hsa-miR-802-002004
hsa-miR-871-002354
hsa-miR-872-002264
hsa-miR-873-002356
hsa-miR-874-0022687.9572
hsa-miR-875-3p-002204
hsa-miR-876-3p-002225
hsa-miR-876-5p-002205
hsa-miR-885-3p-002372
hsa-miR-886-3p-0021941.2122
hsa-miR-887-002374
hsa-miR-888#-002213
hsa-miR-888-002212
hsa-miR-889-002202
hsa-miR-890-002209
hsa-miR-891a-0021914.6329
hsa-miR-891b-002210
hsa-miR-892a-002195
hsa-miR-892b-002214
hsa-miR-9#-002231
hsa-miR-9-000583
hsa-miR-920-002150
hsa-miR-921-002151
hsa-miR-922-002152
hsa-miR-924-002154
hsa-miR-92a-1#-002137
hsa-miR-92a-2#-002138
hsa-miR-92b#-002343
hsa-miR-933-002176
hsa-miR-934-002177
hsa-miR-936-002179
hsa-miR-937-002180
hsa-miR-938-002181
hsa-miR-943-002188
hsa-miR-944-002189
hsa-miR-95-000433
hsa-miR-96#-002140
hsa-miR-98-000577
hsa-miR-99a#-0021410.0374
hsa-miR-99b#-0021960.0378
mmu-let-7d#-001178
mmu-miR-124a-001182
mmu-miR-129-3p-0011840.2208
mmu-miR-137-001129
mmu-miR-153-001191
mmu-miR-187-001193
mmu-miR-379-001138
mmu-miR-496-001953
mmu-miR-499-001352

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Figure 1. Fold change of miRNA concentration between GD group and control. X-axis—miRNAs, Y-axis—fold change.
Figure 1. Fold change of miRNA concentration between GD group and control. X-axis—miRNAs, Y-axis—fold change.
Life 11 00002 g001
Table 1. Clinical characteristics of study participants with GD (n = 20) and the control group (n = 10).
Table 1. Clinical characteristics of study participants with GD (n = 20) and the control group (n = 10).
CharacteristicGD PatientsControlp-Value
Sex (F/M)60/40%50/50%0.53
Age (years)23–73 (median: 36.6)24–60 (median: 37.5)0.43
First symptoms (age)1–55 (median: 16)N/AN/A
Weight (kg)39–71 (median: 63)59–89 (median: 71)0.53
Height (cm)138–175 (median: 162)164–181 (median: 169)0.73
BMI (kg/m2)20.5–25.5 (median: 24.81)20.9–29 (median: 23.59)0.20
Splenectomy2/18 (10%)0/10 (0%)0.54
Table 2. 30 significantly altered microRNA level in GD patient’s group vs control; U6 was used as a reference.
Table 2. 30 significantly altered microRNA level in GD patient’s group vs control; U6 was used as a reference.
miRNAGaucher vs. U6
Fold Changep
hsa-let-7g-5p6.28150.0323
hsa-miR-103-3p4.89080.0053
hsa-miR-106a-5p14.78740.0082
hsa-miR-126-3p21.39740.0362
hsa-miR-130b-3p4.23820.0303
hsa-miR-140-3p5.29850.0446
hsa-miR-148a-3p27.54810.0158
hsa-miR-148b-3p9.91130.0076
hsa-miR-152-3p17.72940.0241
hsa-miR-155-5p11.87670.0016
hsa-miR-16-5p21.45190.0343
hsa-miR-17-5p14.43250.0084
hsa-miR-182-5p8.51830.0001
hsa-miR-183-5p6.72240.0059
hsa-miR-185-5p15.96170.0206
hsa-miR-195-5p20.10850.0083
hsa-miR-199a-3p18.82140.0462
hsa-miR-20a-5p14.48070.0079
hsa-miR-20b-5p13.67560.0317
hsa-miR-210-3p4.28880.0299
hsa-miR-22-3p6.99430.0246
hsa-miR-24-3p7.40020.0091
hsa-miR-26b-5p57.020.0351
hsa-miR-27b-3p5.1720.0081
hsa-miR-29a-3p49.19230.0239
hsa-miR-31-5p51.03220.0004
hsa-miR-363-3p18.28580.0267
hsa-miR-454-3p35.83310.0242
hsa-miR-532-5p5.60110.0102
hsa-miR-660-5p28.12580.0357
Table 3. KEGG pathway analysis of miRNA target genes, significantly altered with p < 0.05.
Table 3. KEGG pathway analysis of miRNA target genes, significantly altered with p < 0.05.
#KEGG Pathwayp-Value#Genes#miRNAs
Fatty acid biosynthesis<0.000142
Prion diseases<0.000114
ECM-receptor interaction<0.0001459
Glioma<0.00013511
Signaling pathways regulating pluripotency of stem cells<0.0001698
Proteoglycans in cancer<0.00019711
TGF-beta signaling pathway<0.0001439
FoxO signaling pathway<0.0001499
Metabolism of xenobiotics by cytochrome P450<0.0001135
Pathways in cancer0.00010489711186
Prostate cancer0.000309422334
Amoebiasis0.0004413348232
Mucin type O-Glycan biosynthesis0.001082663124
PI3K-Akt signaling pathway0.001397357897
Focal adhesion0.001524581704
Renal cell carcinoma0.002187101195
Melanoma0.002618092265
Fatty acid metabolism0.00569376472
Axon guidance0.01611344434
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Pawliński, Ł.; Polus, A.; Tobór, E.; Sordyl, M.; Kopka, M.; Solnica, B.; Kieć-Wilk, B. MiRNA Expression in Patients with Gaucher Disease Treated with Enzyme Replacement Therapy. Life 2021, 11, 2. https://doi.org/10.3390/life11010002

AMA Style

Pawliński Ł, Polus A, Tobór E, Sordyl M, Kopka M, Solnica B, Kieć-Wilk B. MiRNA Expression in Patients with Gaucher Disease Treated with Enzyme Replacement Therapy. Life. 2021; 11(1):2. https://doi.org/10.3390/life11010002

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Pawliński, Łukasz, Anna Polus, Ewa Tobór, Maria Sordyl, Marianna Kopka, Bogdan Solnica, and Beata Kieć-Wilk. 2021. "MiRNA Expression in Patients with Gaucher Disease Treated with Enzyme Replacement Therapy" Life 11, no. 1: 2. https://doi.org/10.3390/life11010002

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