Alteration of the micro RNA network during the progression of Alzheimer's disease
2013; Springer Nature; Volume: 5; Issue: 10 Linguagem: Inglês
10.1002/emmm.201201974
ISSN1757-4684
AutoresPierre Lau, Koen Bossers, Rekin’s Janky, Evgenia Salta, Carlo Sala Frigerio, Shahar Barbash, Roy Rothman, Annerieke Sierksma, Amantha Thathiah, David Greenberg, Aikaterini S. Papadopoulou, Tilmann Achsel, Torik Ayoubi, Hermona Soreq, Joost Verhaagen, Dick F. Swaab, Stein Aerts, Bart De Strooper,
Tópico(s)Genomics, phytochemicals, and oxidative stress
ResumoResearch Article9 September 2013Open Access Alteration of the microRNA network during the progression of Alzheimer's disease Pierre Lau Pierre Lau VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Koen Bossers Koen Bossers Neurogeneration Group, Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands Search for more papers by this author Rekin's Janky Rekin's Janky Laboratory of Computational Biology, Center for Human Genetics and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Evgenia Salta Evgenia Salta VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Carlo Sala Frigerio Carlo Sala Frigerio VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Shahar Barbash Shahar Barbash Department of Biological Chemistry, the Silberman Institute of Life Sciences, and the Edmond and Lily Safra Center of Brain Science Interdisciplinary Center for Neural Computation, Jerusalem, Israel Search for more papers by this author Roy Rothman Roy Rothman Department of Biological Chemistry, the Silberman Institute of Life Sciences, and the Edmond and Lily Safra Center of Brain Science Interdisciplinary Center for Neural Computation, Jerusalem, Israel Search for more papers by this author Annerieke S. R. Sierksma Annerieke S. R. Sierksma VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Amantha Thathiah Amantha Thathiah VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author David Greenberg David Greenberg Department of Biological Chemistry, the Silberman Institute of Life Sciences, and the Edmond and Lily Safra Center of Brain Science Interdisciplinary Center for Neural Computation, Jerusalem, Israel Search for more papers by this author Aikaterini S. Papadopoulou Aikaterini S. Papadopoulou VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Tilmann Achsel Tilmann Achsel VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Torik Ayoubi Torik Ayoubi VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Saint James School of Medicine, Plaza Juliana, Kralendijk, Bonaire, Dutch Caribbean, The Netherlands Search for more papers by this author Hermona Soreq Hermona Soreq Department of Biological Chemistry, the Silberman Institute of Life Sciences, and the Edmond and Lily Safra Center of Brain Science Interdisciplinary Center for Neural Computation, Jerusalem, Israel Search for more papers by this author Joost Verhaagen Joost Verhaagen Neurogeneration Group, Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands Search for more papers by this author Dick F. Swaab Dick F. Swaab Neuropsychiatric Disorders Group, Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands Search for more papers by this author Stein Aerts Stein Aerts Laboratory of Computational Biology, Center for Human Genetics and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Bart De Strooper Corresponding Author Bart De Strooper VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Pierre Lau Pierre Lau VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Koen Bossers Koen Bossers Neurogeneration Group, Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands Search for more papers by this author Rekin's Janky Rekin's Janky Laboratory of Computational Biology, Center for Human Genetics and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Evgenia Salta Evgenia Salta VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Carlo Sala Frigerio Carlo Sala Frigerio VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Shahar Barbash Shahar Barbash Department of Biological Chemistry, the Silberman Institute of Life Sciences, and the Edmond and Lily Safra Center of Brain Science Interdisciplinary Center for Neural Computation, Jerusalem, Israel Search for more papers by this author Roy Rothman Roy Rothman Department of Biological Chemistry, the Silberman Institute of Life Sciences, and the Edmond and Lily Safra Center of Brain Science Interdisciplinary Center for Neural Computation, Jerusalem, Israel Search for more papers by this author Annerieke S. R. Sierksma Annerieke S. R. Sierksma VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Amantha Thathiah Amantha Thathiah VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author David Greenberg David Greenberg Department of Biological Chemistry, the Silberman Institute of Life Sciences, and the Edmond and Lily Safra Center of Brain Science Interdisciplinary Center for Neural Computation, Jerusalem, Israel Search for more papers by this author Aikaterini S. Papadopoulou Aikaterini S. Papadopoulou VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Tilmann Achsel Tilmann Achsel VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Torik Ayoubi Torik Ayoubi VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Saint James School of Medicine, Plaza Juliana, Kralendijk, Bonaire, Dutch Caribbean, The Netherlands Search for more papers by this author Hermona Soreq Hermona Soreq Department of Biological Chemistry, the Silberman Institute of Life Sciences, and the Edmond and Lily Safra Center of Brain Science Interdisciplinary Center for Neural Computation, Jerusalem, Israel Search for more papers by this author Joost Verhaagen Joost Verhaagen Neurogeneration Group, Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands Search for more papers by this author Dick F. Swaab Dick F. Swaab Neuropsychiatric Disorders Group, Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands Search for more papers by this author Stein Aerts Stein Aerts Laboratory of Computational Biology, Center for Human Genetics and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Bart De Strooper Corresponding Author Bart De Strooper VIB Center for the Biology of Disease, Leuven, Belgium Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium Search for more papers by this author Author Information Pierre Lau1,2, Koen Bossers3, Rekin's Janky4, Evgenia Salta1,2, Carlo Sala Frigerio1,2, Shahar Barbash5, Roy Rothman5, Annerieke S. R. Sierksma1,2, Amantha Thathiah1,2, David Greenberg5, Aikaterini S. Papadopoulou1,2, Tilmann Achsel1,2, Torik Ayoubi1,2,6, Hermona Soreq5, Joost Verhaagen3, Dick F. Swaab7, Stein Aerts4 and Bart De Strooper 1,2 1VIB Center for the Biology of Disease, Leuven, Belgium 2Center for Human Genetics, Leuven Institute for Neurodegenerative Disorders (LIND) University Hospitals Leuven, and University of Leuven, O&N4, Herestraat Leuven, Belgium 3Neurogeneration Group, Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands 4Laboratory of Computational Biology, Center for Human Genetics and University of Leuven, O&N4, Herestraat Leuven, Belgium 5Department of Biological Chemistry, the Silberman Institute of Life Sciences, and the Edmond and Lily Safra Center of Brain Science Interdisciplinary Center for Neural Computation, Jerusalem, Israel 6Saint James School of Medicine, Plaza Juliana, Kralendijk, Bonaire, Dutch Caribbean, The Netherlands 7Neuropsychiatric Disorders Group, Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands *Corresponding author: Tel: +32 16 373 101; Fax: +32 16 330 827;E-mail: [email protected] EMBO Mol Med (2013)5:1613-1634https://doi.org/10.1002/emmm.201201974 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info An overview of miRNAs altered in Alzheimer's disease (AD) was established by profiling the hippocampus of a cohort of 41 late-onset AD (LOAD) patients and 23 controls, showing deregulation of 35 miRNAs. Profiling of miRNAs in the prefrontal cortex of a second independent cohort of 49 patients grouped by Braak stages revealed 41 deregulated miRNAs. We focused on miR-132-3p which is strongly altered in both brain areas. Downregulation of this miRNA occurs already at Braak stages III and IV, before loss of neuron-specific miRNAs. Next-generation sequencing confirmed a strong decrease of miR-132-3p and of three family-related miRNAs encoded by the same miRNA cluster on chromosome 17. Deregulation of miR-132-3p in AD brain appears to occur mainly in neurons displaying Tau hyper-phosphorylation. We provide evidence that miR-132-3p may contribute to disease progression through aberrant regulation of mRNA targets in the Tau network. The transcription factor (TF) FOXO1a appears to be a key target of miR-132-3p in this pathway. INTRODUCTION Alzheimer's disease (AD) is a progressive age-related dementia characterized by amyloid plaques, neuronal tangles, neurofibrillary degeneration and vascular amyloidopathy in the brain. The cause of early-onset AD (EOAD) is well known with mutations in the APP, PSEN1 and PSEN2 genes, contributing to the accumulation of amyloid plaques. In late-onset AD (LOAD), genome-wide association studies (GWAS) recently identified more than ten other loci [reviewed in (Tanzi, 2012)], suggesting the existence of additional molecular pathways contributing to the disease. MicroRNAs (miRNAs) are short ∼22 nt RNA molecules that bind to the transcripts of protein-coding genes to direct their post-transcriptional repression and by that regulate important physiological and pathophysiological signalling pathways. Increasing evidence links aberrant expression of miRNAs to neurodegenerative disorders including AD [reviewed in (Lau & de Strooper, 2010)]. For instance, miR-29b was found to be downregulated in the anterior temporal cortex of a subgroup of AD patients with high BACE1 protein expression (Hebert et al, 2008) and decreased miR-107 was also observed in the temporal cortex of some AD cases (Wang et al, 2008). However, the small number of patients analyzed represents a major issue when identifying which miRNAs are deregulated during disease. Confounding factors, in particular the technologies used for miRNA profiling which provide deviating results (Pritchard et al, 2012), have to be considered as well. A more systematic analysis of miRNAs is clearly needed to determine which miRNAs and molecular networks normally controlled by such miRNAs are affected during disease [reviewed in (Salta & De Strooper, 2012)]. We aimed here to determine miRNA alterations in LOAD by profiling two large and independent cohorts of patients using the nCounter system, a technology proven to reliably quantify miRNAs (Wyman et al, 2011). We found numerous changes in the expression of miRNAs in the hippocampus and prefrontal cortex of LOAD patients. Of importance, downregulation of miR-132-3p stands out by robustness and consistency. This observation was confirmed by real-time PCR on the two same brain areas initially investigated and for the temporal gyrus. In agreement, downregulation of miR-132-3p in the LOAD prefrontal cortex was also found by next-generation sequencing of miRNAs and by in situ hybridization. We also provide initial identification of miR-132-3p targets of relevance to LOAD, thus offering novel insights into the pathogenesis of the disease. RESULTS Deregulation of miRNAs in the hippocampus of LOAD patients We analyzed the miRNA expression profile of a first cohort made of 41 LOAD cases and 23 age-matched controls (clinical data in Supporting Information Table S1). The quality of the total RNA obtained from these post-mortem samples was systematically assessed (Supporting Information Fig S1A). The RNA Integrity Number (RIN) values were relatively low, indicating fragmented total RNA. However, raw data analysis showed that global expression of miRNAs in the samples with lower (2 < RIN < 6) and higher RIN values (RIN > 6) was similar (Supporting Information Figs S1B and S2A), therefore indicating that miRNAs were relatively resistant to RNA degradation. Because of the discrete counting nature of the nCounter system used for miRNA profiling, positive skewness (Supporting Information Fig S2B) and overdispersion of the data (Supporting Information Fig S3B), a statistical model based on the negative binomial distribution as implemented in the DESeq package (Anders & Huber, 2010) was used to call differential miRNA expression. We found that 35 (5.5%) of 641 tested miRNAs were differentially expressed between the LOAD cases and the control group [padj < 0.05, nbinomTest corrected for multiple testing by the Benjamini–Hochberg (BH) procedure]. Of interest, 20 miRNAs were downregulated and 15 were upregulated in the LOAD group when compared to the controls (Table 1 and Fig 1A). Notably, we identified miR-132-3p as the most significantly downregulated miRNA (padj = 1.57E−07) together with other brain-enriched miRNAs such as miR-128, miR-136-5p, miR-138-5p, miR-124-3p, miR-129-5p and miR-129-2-3p. Among the upregulated miRNAs in the LOAD group and with previous reported expression in the brain, we found miR-27a-3p, miR-142-3p, miR-92b-3p and miR-200a-3p (Table 1 and Fig 1A). Table 1. Deregulated miRNAs in the hippocampus of LOAD patients miRNA Control LOAD Fold Change log2 Fold Change pval padj hsa-miR-132-3p 4316 2541 0.59 −0.76 6.35E-10 1.57E-07 hsa-miR-128 3995 2443 0.61 −0.71 1.42E-07 1.76E-05 hsa-miR-23a-3p 1207 1876 1.55 0.64 1.44E-05 0.0011903 hsa-miR-455-5p 14 40 2.95 1.56 2.72E-05 0.0016864 hsa-miR-129-5p 539 346 0.64 −0.64 3.51E-05 0.0017411 hsa-miR-363-3p 157 251 1.60 0.68 5.96E-05 0.0024648 hsa-miR-27a-3p 129 215 1.67 0.74 0.0001669 0.0059128 hsa-miR-370 92 51 0.55 −0.85 0.0002451 0.0067535 hsa-miR-487b 1860 1321 0.71 −0.49 0.0002444 0.0067535 hsa-let-7f-5p 4040 5712 1.41 0.50 0.0002978 0.0072293 hsa-miR-223-3p 613 885 1.44 0.53 0.0003366 0.0072293 hsa-miR-433 330 213 0.65 −0.63 0.0003498 0.0072293 hsa-miR-195-5p 590 843 1.43 0.51 0.0004041 0.0077096 hsa-miR-138-5p 130 80 0.62 −0.70 0.0004766 0.008442 hsa-miR-142-3p 821 1358 1.66 0.73 0.0005951 0.0098391 hsa-miR-129-2-3p 2245 1606 0.72 −0.48 0.0007848 0.0117644 hsa-miR-150-5p 463 664 1.43 0.52 0.0008064 0.0117644 hsa-miR-136-5p 1534 1135 0.74 −0.43 0.0012255 0.0168841 hsa-let-7i-5p 3802 5118 1.35 0.43 0.0014724 0.0175449 hsa-miR-124-3p 306 205 0.67 −0.58 0.0014857 0.0175449 hsa-miR-362-3p 98 151 1.54 0.62 0.001356 0.0175449 hsa-miR-92b-3p 1060 1529 1.44 0.53 0.0016005 0.0180415 hsa-miR-127-3p 346 239 0.69 −0.54 0.0017286 0.0186383 hsa-miR-329 240 160 0.67 −0.58 0.0019822 0.0196638 hsa-miR-495-3p 1946 1454 0.75 −0.42 0.0019607 0.0196638 hsa-miR-409-5p 46 25 0.53 −0.92 0.0021989 0.0209741 hsa-miR-487a 506 369 0.73 −0.45 0.0026776 0.0245943 hsa-miR-410 647 481 0.74 −0.43 0.0028013 0.0248112 hsa-miR-543 432 316 0.73 −0.45 0.0040771 0.034866 hsa-miR-199a-3p 88 138 1.57 0.65 0.0048687 0.0389497 hsa-miR-199b-3p 88 138 1.57 0.65 0.0048687 0.0389497 hsa-miR-769-5p 47 27 0.57 −0.81 0.0053295 0.0413038 hsa-miR-219-2-3p 2244 1711 0.76 −0.39 0.0060143 0.0443437 hsa-miR-425-5p 147 98 0.67 −0.58 0.0060794 0.0443437 hsa-miR-200a-3p 53 103 1.94 0.96 0.0067664 0.0479449 The name of the mature miRNAs is in accordance with the miRBase V19 nomenclature. Control: normalized counts for the control group; LOAD: normalized counts for the LOAD group; Fold Change and log2 Fold Change correspond to the difference between the LOAD and Control groups; pval: nominal p-value determined by the nbinomTest; padj: nominal p-value corrected for multiple testing by the BH procedure. Figure 1.Differential expression of miRNAs in late-onset AD (LOAD) hippocampus Hierarchical clustering of deregulated miRNAs. Thirty-five significant miRNAs (p < 0.05, nbinomTest after BH adjustment) obtained from 64 hippocampus samples were hierarchically clustered using Spearman's correlation. Horizontal bar above the heatmap indicates the control (green) and LOAD (red) groups as defined by pathologic examination. The sample identifiers are indicated at the bottom of the heatmap and miRNAs are found on the right side. Blue colour corresponds to low miRNA expression values and red denotes high expression values as indicated by the colour key. Probability prediction of the hippocampus samples. The Support Vector Machine (SVM) training algorithm was built on a subset of 10 LOAD and 10 controls considering the 35 differentially expressed miRNAs shown in panel A. The obtained predictors were used to test the remaining 31 LOAD cases and 13 control samples. The index corresponds to each patient and the vertical dashed line delineates the control (left) and LOAD (right) groups. The y-axis represents the probability of diagnosis for each of the samples as determined by the SVM classifier (predicted class). Blue and red dots correspond to the probability that the sample belongs to the control group or the LOAD group, respectively. A sample is considered as misclassified when the probability for the predicted class is higher than and different from the probability corresponding to the real class (as defined by clinical diagnosis). By using this binary SVM classifier, three LOAD samples were misclassified as controls and all the controls were correctly called. Receiver operating characteristic (ROC) curve of the hippocampus-derived SVM classifier. The ROC curve summarizes the accuracy of the SVM built on the 35 differentially expressed miRNAs using a number of cross-validation equals to the number of predictions (n = 44). This set of miRNAs was predictive of LOAD with a sensitivity of 90% and a specificity of 100%. The area under the curve (AUC) was 0.96, with the best possible value would be one and any non-random prediction would be more than 0.5. Following standard convention, the true positive rate is defined as sensitivity and the false positive rate as (1-specificity). Download figure Download PowerPoint A supervised clustering based on Support Vector Machine (SVM) was built to evaluate whether the 35 differentially expressed miRNAs could be used to classify the hippocampus samples. After training with a subset consisting of 10 LOAD cases and 10 controls, the SVM classifier was tested on the 44 remaining samples including 31 LOAD cases and 13 controls. Among the 44 samples, three (X7H, X27H, X30H) LOAD cases were misclassified as controls whereas all the control samples were correctly assigned, resulting in a sensitivity of 90% and a specificity of 100% for this assay (Fig 1B). The receiver operating characteristic (ROC) curve further showed an area under the curve (AUC) of 0.96 (Fig 1C), confirming the potential application of these 35 miRNAs as markers of LOAD. Temporal analysis of miRNAs in the prefrontal cortex of LOAD patients We next studied 49 prefrontal cortex samples from a second cohort of clinically well-defined patients stratified according to the six classical Braak stages (BRI to VI) and of seven control samples (BR0) (clinical data in Supporting Information Table S2) (Bossers et al, 2010). The global cellular composition in this brain area remains well preserved over different Braak stages in comparison to the hippocampus. Quality assessment showed excellent total RNA integrity for those samples when compared to those in the first cohort studied (Supporting Information Fig S1A). Global expression profile of miRNAs for the 49 prefrontal cortex samples was similar, irrespective of the Braak stages and of the quality of the RNA (Supporting Information Fig S1C). Skewness and overdispersion of the nCounter data were observed (Supporting Information Fig S3A and C). We therefore applied a general linear model (GLM) based on the negative binomial distribution to find differentially expressed miRNAs between any two Braak stages. In total, 41 (6.4%) of 641 tested miRNAs were found to be changed (padj < 0.05, nbinomGLMTest after BH correction) (Table 2 and Fig 2A). The expression analysis of those 41 significantly altered miRNAs showed dynamic expression patterns that were characteristic of downregulated (clusters 1, 2 and 3) and upregulated (cluster 4) miRNAs (Fig 2B). Of importance, some of the miRNA changes were observed at early BRI and BRII stages. For instance, the eight miRNAs in cluster 1, including miR-132-3p, were upregulated between BRI and BRII, and then downregulated between BRII and BRVI. Cluster 2 was mainly characterized by a small loss between BRII and BRIV, followed by a steep decline between late BRV and BRVI stages. Eleven miRNAs such as miR-129-5p, miR-129-2-3p, miR-136-5p, miR-370, miR-409-5p and miR-487a belong to this cluster 2. Cluster 3 consisted of 10 miRNAs showing a gradual downregulation between BR0/BRI and BRVI. Lastly, cluster 4 was made of 12 upregulated miRNAs such as miR-27a-3p, miR-92b-3p and miR-200a-3p. The complete list of miRNAs in each of the four clusters is included in Table 2. In addition, a comparison of miRNAs found to be deregulated between any two Braak stages by the nbinomGLMTest implemented in DESeq to those found by two other popular algorithms, i.e. edgeR and Voom + Limma, shows that 40 of 41 miRNAs found to be changed in the prefrontal cortex by DESeq were also called by at least one of the two other methods (Supporting Information Fig S4), therefore supporting the robustness of the analysis. Table 2. Deregulated miRNAs in the prefrontal cortex of LOAD patients miRNA BR0 BRI BRII BRIII BRIV BRV BRVI pval padj Cluster hsa-miR-132-3p 6405 5518 7889 5638 3387 3367 2409 7.377E-12 2.279E-09 1 hsa-miR-127-5p 34 27 30 20 15 18 0 2.882E-10 3.98E-08 3 hsa-miR-1321 16 11 25 8 0 2 2 3.864E-10 3.98E-08 1 hsa-miR-210 19 38 22 28 8 5 0 6.376E-08 4.925E-06 3 hsa-miR-214-3p 82 80 24 134 116 144 266 6.686E-07 4.132E-05 4 hsa-miR-496 36 36 41 36 21 31 5 3.796E-06 0.0001955 2 hsa-miR-1178-3p 20 19 16 5 15 7 0 2.605E-05 0.0010853 3 hsa-miR-133b 18 13 21 6 3 7 1 2.968E-05 0.0010853 1 hsa-miR-337-3p 47 52 51 42 54 32 11 3.161E-05 0.0010853 2 hsa-miR-421 40 39 51 40 21 21 10 4.078E-05 0.0012602 1 hsa-miR-548j 26 20 16 15 9 11 1 0.0001314 0.0036922 3 hsa-miR-200a-3p 220 184 156 264 262 299 487 0.0001487 0.0038301 4 hsa-miR-431-5p 30 35 44 25 20 34 7 0.000173 0.0041117 2 hsa-miR-744-5p 187 173 141 246 225 234 326 0.0002172 0.004794 4 hsa-miR-491-3p 23 26 21 11 15 15 2 0.0002616 0.0053892 3 hsa-miR-633 48 42 137 10 3 15 1 0.0003924 0.0075776 1 hsa-miR-485-5p 43 45 41 28 24 38 10 0.0004575 0.0078541 3 hsa-miR-508-3p 17 18 23 14 9 8 1 0.0004413 0.0078541 1 hsa-miR-1275 32 22 5 36 28 26 33 0.0005612 0.0091191 4 hsa-miR-520a-3p 18 20 12 5 5 5 2 0.0006197 0.0091191 3 hsa-miR-758-3p 36 31 28 24 22 14 6 0.0006192 0.0091191 3 hsa-miR-135b-5p 77 80 77 71 60 59 27 0.0006627 0.0093078 2 hsa-miR-1179 23 17 29 15 10 10 2 0.000704 0.0094586 1 hsa-miR-1260a 3320 3175 2392 4873 4274 4708 6518 0.0007804 0.0100482 4 hsa-miR-551b-3p 43 63 41 36 37 25 12 0.0012518 0.0154719 3 hsa-miR-10b-5p 18 19 23 8 6 13 3 0.0013402 0.0159276 1 hsa-miR-129-2-3p 2597 2503 2661 2472 2400 2422 1627 0.0017505 0.0200339 2 hsa-miR-370 144 135 174 135 118 131 74 0.0018428 0.0203364 2 hsa-miR-190b 298 273 241 233 194 286 365 0.0021923 0.0233596 4 hsa-miR-517c-3p 105 110 110 128 180 176 103 0.0026103 0.026019 4 hsa-miR-519a-3p 105 110 110 128 180 176 103 0.0026103 0.026019 4 hsa-miR-129-5p 1140 1080 1199 1147 986 998 737 0.0029627 0.028176 2 hsa-miR-219-1-3p 12 19 14 5 1 4 4 0.0030091 0.028176 3 hsa-miR-409-5p 93 69 92 88 68 71 38 0.0044917 0.0408215 2 hsa-miR-27a-3p 378 326 477 399 432 412 561 0.0046685 0.0412159 4 hsa-miR-424-5p 123 99 87 125 100 119 180 0.0051385 0.0441053 4 hsa-miR-487a 557 506 618 621 458 513 385 0.0053237 0.0444603 2 hsa-miR-382-5p 49 62 51 56 42 56 18 0.0055054 0.0447675 2 hsa-miR-92b-3p 1334 1357 1335 1490 1530 1658 1981 0.0061121 0.048427 4 hsa-miR-136-5p 1061 1241 1203 1242 1184 1060 803 0.0063022 0.0486845 2 hsa-miR-874 218 157 134 244 206 207 263 0.0064902 0.0489137 4 The values represent the mean of the normalized miRNA counts at each Braak stage for the significant miRNAs. pval: nominal p-value (nbinomGLMTest); padj: nominal p-value corrected for multiple testing using the BH procedure; Cluster: Fuzzy clustering of the significant miRNAs into four classes. Figure 2.Differential expression of miRNAs in LOAD prefrontal cortex Hierarchical clustering of deregulated miRNAs across the Braak stages. Forty one miRNAs were found to differ between any two Braak stages (p < 0.05, nbinomGLMTest after BH adjustment). Hierarchical clustering of miRNAs was obtained using Spearman's correlation distance metric and average linkage. The samples are indicated at the bottom of the heatmap and miRNAs are found at the right side. Red colour in the colour key corresponds to high expression and blue colour to low expression values. The four colours at the top of the heatmap indicate the controls (BR0), early-stages (BRI and BRII), mid-stages (BRIII and BRIV) and late-stages (BRV and BRVI) of disease. Time-course analysis of deregulated miRNAs across the Braak stages. The expression of the 41 deregulated miRNAs was standardized, clustered into four groups and plotted according to the control (BR0) and the six Braak stages. Clusters 1, 2 and 3 correspond to downregulated miRNAs during disease whereas cluster 4 contains the upregulated miRNAs. Downregulation was evident between BRII and III stages for the miRNAs found in cluster 1 whilst a steep decline between BRV and VI characterized the cluster 2. Cluster 3 contains miRNAs gradually downregulated during disease. Cluster 4 was the only group corresponding to some upregulate
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