Abstract

Tuberous sclerosis complex (TSC) is a multisystem genetic disorder caused by mutations in the TSC1 and TSC2 genes. Over 80% of TSC patients are affected by epilepsy, but the molecular events contributing to seizures in TSC are not well understood. Recent reports have demonstrated that the brain is enriched with microRNA activity, and they are critical in neural development and function. However, little is known about the role of microRNAs in TSC. Here, we report the characterization of aberrant microRNA activity in cortical tubers resected from 5 TSC patients surgically treated for medically intractable epilepsy. By comparing epileptogenic tubers with adjacent nontuber tissue, we identified a set of 4 coordinately overexpressed microRNAs (miRs 23a, 34a, 34b*, 532-5p). We used quantitative liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomic profiling to investigate the combined effect of the 4 microRNAs on target proteins. The proportion of repressed proteins among the predicted targets was significantly greater than in the overall proteome and was highly enriched for proteins involved in synaptic signal transmission. Among the combinatorial targets were TSC1, coding for the protein hamartin, and several epilepsy risk genes. We found decreased levels of hamartin in epileptogenic tubers and confirmed targeting of the TSC1 3′ UTR by miRs-23a and 34a.

Introduction

Tuberous sclerosis complex (TSC) is an autosomal dominant genetic disorder resulting from mutations in the TSC genes, leading to activation of mammalian/mechanistic target of rapamycin (mTOR), and is characterized by hamartomatous lesions in many organs, including brain lesions termed tubers (Roach and Sparagana 2004). Tubers are characterized by abnormal cortical lamination and the presence of dysmorphic neurons and balloon cells. Epilepsy afflicts over 80% of TSC patients, and many suffer seizures that do not respond to pharmacological therapy and require surgical resection of epileptogenic tubers (Thiele 2004; Weiner et al. 2004). The molecular events that contribute to seizures in TSC are not well characterized.

MicroRNAs are endogenous noncoding RNAs that provide post-transcriptional regulation of many genes and most cell processes. Approximately one-third of all protein-coding genes are believed to be regulated by microRNAs, and aberrant microRNA expression has been implicated in a wide range of diseases (Filipowicz et al. 2008). Mature microRNAs are ∼22 nucleotides in length and anneal to complementary sites in the 3′ untranslated region (UTR) of target transcripts as part of an RNA-induced silencing complex, resulting in either transcript degradation or inhibition of protein translation. There are currently over 2000 known mature microRNAs in the human genome, and each microRNA may regulate dozens to hundreds of target transcripts. The 3′ UTR in a single-messenger RNA may contain binding sites for numerous microRNAs, and a transcript can be concurrently repressed by multiple microRNA species. Compared with other organs, the brain is highly enriched for microRNA activity, likely due to the remarkable level of structural and functional complexity in the tissue (Fineberg et al. 2009). Numerous microRNAs are differentially expressed during corticogenesis and neuronal differentiation (Krichevsky et al. 2003; Nielsen et al. 2009). Recent reports have also implicated microRNAs in epileptogenesis (Jimenez-Mateos et al. 2012; You et al. 2012). However, little is known about the involvement of microRNAs in TSC, and there are currently no published reports on their role in cortical tubers. A potential role for microRNAs in neurological manifestations of TSC is suggested by the prominent role of microRNAs in neurodevelopment and evidence indicating microRNA regulation downstream of mTOR. MicroRNAs have been reported to be regulated by p53 in different cell types, and p53 levels and activity are regulated by mTOR (Lee et al. 2007; Shin et al. 2012). In addition, inflammation, which is present in epilepsy and cortical tubers (Maldonado et al. 2003; Aronica and Crino 2011), has been shown to induce microRNA expression through inflammatory cytokines (Junker et al. 2009). In this work, we characterized microRNA expression patterns in cortical tubers resected to treat intractable epilepsy in TSC patients and compared expression levels with patient-matched adjacent nontuber tissue that was concurrently resected due to epileptiform electroencephalogram (EEG) activity. Furthermore, we used quantitative proteomics to assess the effects of altered microRNA levels on protein expression, as well as investigate the potential roles for p53 and inflammation as mechanisms involved in regulating microRNA levels in TSC.

Materials and Methods

TSC Brain Tissue

Microarray analysis was performed on brain tissue samples obtained from 5 patients who underwent epilepsy surgery for medically refractory epilepsy at Children's Hospital of Michigan, Detroit. Table 1 shows patient demographics and mutation types. Inclusion criteria were: 1) diagnosis of TSC defined by clinical criteria developed in a consensus conference (Roach et al. 1999); and 2) resective surgery for treatment of medically refractory epilepsy; 3) both epileptogenic tuber and adjacent nontuber tissue available; and 4) written informed consent of parent or guardian. Exclusion criteria were: history of previous brain surgery that may have affected the original epileptic focus (including epilepsy surgery, surgery for subependymal giant cell astrocytoma, shunt placement for hydrocephalus). Preoperative assessment included clinical evaluation, neuroimaging using magnetic resonance imaging (MRI) and positron emission tomography (PET) with alpha[C-11]methyl-l-tryptophan (AMT) (Chugani et al. 2013), and ictal/interictal EEG. Two tissue types were sampled from the surgical resection determined by clinical parameters for each patient: 1) tuber characterized by independent epileptiform activity (seizure onset); and 2) nontuber tissue adjacent to the epileptogenic tuber (nononset). Portions of each sample from frozen blocks were immediately placed in extraction buffer for RNA and protein isolation. All protocols were approved by the Human Investigation Committee at Wayne State University.

Table 1

Patient demographics

Identifier Gender Age Gene Location of resected tissue Mutation 
A1409 2 years 6 months TSC1 Right temporal Frameshift, 8-bp insertion, nt 1609 
E3007 10 years TSC1 Left frontal Frameshift, 5-bp deletion, nt 1020–1024 
H2407 8 years 9 months TSC2 Left temporal Frameshift, 1-bp deletion nt 5444 
81603 8 years 11 months TSC2 Right frontal Transition nt 2410 t->c, codon 804 Cys->Arg 
90602 7 years 10 months TSC2 Left occipital/parietal Transition nt 5227 c->t, codon 1743 Arg -> Trp 
Identifier Gender Age Gene Location of resected tissue Mutation 
A1409 2 years 6 months TSC1 Right temporal Frameshift, 8-bp insertion, nt 1609 
E3007 10 years TSC1 Left frontal Frameshift, 5-bp deletion, nt 1020–1024 
H2407 8 years 9 months TSC2 Left temporal Frameshift, 1-bp deletion nt 5444 
81603 8 years 11 months TSC2 Right frontal Transition nt 2410 t->c, codon 804 Cys->Arg 
90602 7 years 10 months TSC2 Left occipital/parietal Transition nt 5227 c->t, codon 1743 Arg -> Trp 

RNA Isolation and Quality

Total RNA, including small RNAs, was isolated from brain tuber and nontuber samples using 25 mg of each tissue sample and the “All-in-One Purification” kit (Norgen Biotek, Thorold, ON, Canada). All steps were performed according to the vendor's protocol. RNase-free DNaseI (Qiagen, Valencia, CA, USA) was used to remove potential gDNA contamination from the samples. RNA integrity was tested using Agilent 2100 Bioanalyzer with the RNA 6000 Nano Assay kit or RNA 6000 Pico Assay kit (Agilent Technologies, Palo Alto, CA, USA). The Bioanalyzer was used to determine if the 18S and 28S ribosomal bands were defined and to ensure minimal degradation of RNA. Optimal concentration of total RNA with the Nano assay was 100–200 ng/μL, and 0.5–1 ng/μL for testing with Pico assay. All steps were done according to the vendor's protocol.

Sample Labeling and microRNA Microarray Hybridization

For each sample, 100 ng of total RNA along with Agilent microRNA spike-in controls were treated with calf intestine alkaline phosphatase (GE Healthcare Bio-Sciences Corp., Piscataway, NJ, USA), and incubated at 37 °C for 30 min. Then the samples were denatured and labeled with Cyanine 3 per the vendor's protocol (Agilent Technologies). The labeled microRNAs were cleaned using micro Bio-Spin 6 columns (Bio-Rad Laboratories, Hercules, CA, USA), and dried in a speed-vac. Dried samples were re-suspended in 17 μL of nuclease-free water. Also, 1 μL of Hyb Spike-in, 4.5 μL of 10× GE Blocking, and 22.5 μL of 2× Hi-RPM hybridization buffer (Agilent) were added, and the hybridization mixture was incubated at 100 °C for 5 min and then put on ice for 5 min. Samples were immediately added to the microarrays in an Agilent SureHyb hybridization chamber and rotated at 20 rpm in a hybridization oven for 20 h at 55 °C. Agilent Human miRNA V3 microarrays were used. Microarrays were processed using the miRNA Microarray System protocol v.2.2 (Agilent Technologies). Slides were scanned using an Agilent dual laser scanner. Tiff images were analyzed using Agilent's feature extraction software (version 10.7.1.1).

Microarray Data Analysis

Microarray data were imported into GeneSpring version 12 for normalization and analysis (Agilent Technologies). The data on each array were quantile normalized, an interarray normalization procedure that ensures equivalent signal distributions for all arrays. Quantile normalization has been shown to be robust and preferable for analysis of microRNA expression in tissue samples (Rao et al. 2008). We then performed a series of stringent filtering and statistical analyses, as outlined below. Filtering microarray data using detection calls and variance metrics are useful methods to improve statistical power while controlling the false discovery rate (Hackstadt and Hess 2009). Following this approach, we selected probes flagged as detected and also having an expression level above the 25th percentile in at least 75% of the samples for at least one condition being compared (tuber or nontuber). Additionally, we filtered out all probes having a coefficient of variation ≤5.0%, calculated from all samples, as these microRNAs are most likely to not be differentially expressed. Statistical analysis was performed using a moderated t-test with the Westfall and Young family-wise error rate (FWER) correction. The FWER indicates the probability that one or more false positives exist among the identified set.

Quantitative RT-PCR of microRNA Expression

cDNAs of all microRNAs were prepared using a single reverse transcription reaction for each sample. About 100 ng total RNA and Universal cDNA Synthesis Kit (Exiqon, Inc., Woburn, MA, USA) were used in a total volume of 20 μL. Incubation at 42 °C for 60 min was followed by heat inactivation at 95 °C for 5 min and immediate cooling to 4 °C. Undiluted cDNA was stored at −20 °C. Immediately before use, cDNA of each sample was ×40 diluted and 8 μL used for the real-time PCR reaction with Exiqon SYBR Green master mix. ROX passive reference dye (Affymetrix, Inc., Santa Clara, CA, USA) was diluted 1:10 in nuclease-free water and 0.4 μL of the ROX dilution was added per 20 μL qPCR reaction. LNA primer sets for miRs-23a, 34a, and 532-5p were obtained from Exiqon. Standard cycling condition for SybrGreen with melting curve analysis was selected as well as manual baseline and threshold setup. The ΔΔCt method was used to quantitate differential expression. We used hsa-miR-26a as an endogenous control in the microRNA qRT-PCR measurements. While miR-26a has been reported as differentially expressed in some malignancies (Gao and Liu 2011), it was utilized as a control in our experiment because it had the lowest coefficient of variation (1.4%) among all microRNAs as calculated from the microarray data of all samples analyzed. We found that mir-26a has considerably less variation in these tissues than U6 snRNA, a frequently used control (data not shown). Additionally, miR-26a has a relatively high level of expression and has been used previously as an endogenous control (Viprey et al. 2012). A two-tailed t-test with equal variance was used to determine statistical significance.

Computational Prediction of microRNA Target Genes and Ontology Analysis

We utilized miRWalk to perform a consensus analysis with 10 algorithms to predict target transcripts (Dweep et al. 2011). For the combinatorial analysis of miRs 34a, 23a, 532-5p, and 34b*, we tabulated, for each target gene, the number of consensus agreements among the 10 algorithms for each of the 4 microRNAs. If multiple sites were predicted for a given microRNA/transcript interaction then the highest scoring site was used. The sum of all 4 microRNAs was used as the total combinatorial score for each transcript. Functional annotation analysis of target gene sets was performed with DAVID (http://david.abcc.ncifcrf.gov) using default statistical settings. To identify putative target genes associated with epilepsy, we utilized 2 public databases: CarpeDB (http://www.carpedb.ua.edu) and epiGAD (http://www.epigad.org). The interaction between miRs-34a, 23a, and 532-5p and the 3′ UTR of TSC1 were modeled using RNAhybrid software (Rehmsmeier et al. 2004).

Luciferase 3′ UTR Reporter Assay

The Cos-7 cell line was used in a dual-luciferase reporter assay (Expresso single shot, Genlantis, San Diego, CA, USA). The full-length TSC1 3′ UTR-luciferase reporter construct (SC222100), a hsa-miR-34a precursor expression plasmid (SC400356), and control expression vector (pCMV-MIR) were obtained from Origene (Rockville, MD, USA). The miR-23a precursor expression plasmid (HmiR0298-MR04) and scrambled expression control vector (CmiR0001-MR04) were purchased from Genecopoeia (Rockville, MD, USA). A pGL4.73 (SV40) Renilla-luc expression vector was used to control for transfection efficiency (#E6911, Promega, Madison, WI, USA). Cells were plated in DMEM (high glucose, Gibco, Life Technologies, Grand Island, NY, USA) with 10% FBS and 1% nonessential amino acids. The cells were plated in a 96-well flat bottom white plate on the day of transfection. After 3-h cells were transfected with Renilla-luc reporter, 3′ UTR-luc reporter, and microRNA expression vector or microRNA expression control vector (empty or scrambled) in the concentration ratio of 0.3:2.5:4.0 (Origene vector) or 0.25:2.5:4.25 (Genecopoeia vector) respectively, with Fugene 6 (Promega) as per manufacturer's instructions. Transfections were performed using at least 3 replicates for each condition. After 48 h of transfection, the Renilla and Firefly luciferase activities were quantified by the Dual Glo luciferase assay system (Promega) measured on a FlexStation 3 universal plate reader (Molecular Devices, Madison, WI, USA). Data analysis was performed using Softmax Pro. The activities of 3′UTR reporter gene were normalized by those of control Renilla-luc and expressed as a fold change by comparing cells transfected with the miR-34a or 23a expression vector to those transfected with empty or scrambled expression vector. A two-tailed t-test with equal variance was used to determine statistical significance.

Protein Extraction

Fresh 1× cell lysis buffer was prepared from 10× cell lysis buffer (p/n 9803, Cell Signaling, Beverly, MA, USA) and protease inhibitor cocktail (DMSO solution) (Sigma p/n P8340) was added in 1:100 dilution. Frozen brain tissue (25–50 mg) was homogenized with a mortar and a pestle for each sample. Liquid nitrogen was added into the mortar during homogenization to keep the tissue frozen. Cold 1× cell lysis buffer (500–800 μL) with protease inhibitor cocktail was added to the frozen tissue powder, mixed and scooped into a 2-mL eppendorf tube. Then the protein extract was centrifuged for 10 min at 14 000 × g in a cold centrifuge. Supernatant was transferred into a fresh 1.5-mL eppendorf tube and smaller aliquots of each protein extract were stored at −80 °C.

Quantitative LC-MS/MS Proteomics Analysis

Each protein sample (95 µg) was reduced with dithiothreitol and alkylated and iodoacetamide (Sigma, St Louis, MO, USA). Samples were then diluted so that the Triton-X concentration was 0.05% and digested with trypsin. Five microgram was analyzed for complete digestion by SDS-PAGE, and the remaining material was desalted using an Oasis 30-mg HLB cartridge (Waters, Milford, MA, USA). A micro BCA assay (ThermoFisher Scientific, Waltham, MA, USA) was used to confirm protein concentrations and then 43 µg of each sample was taken for TMT-10plex labeling (ThermoFisher Scientific). In addition, an equal aliquot of each sample was pooled for a reference sample that was also 43 µg. Eight samples and the reference pool were labeled according to the manufacturer's protocol; one channel was left empty. The labeled samples were combined and 200 µg was taken for analysis. Detergent was removed by serial solid phase extraction using reversed phase, strong cation exchange, and reversed phase materials. An Oasis HLB cartridge (Waters), 4 strong cation exchange spin columns (Nest Group, Southborough, MA) and a Pierce 100 µL C18 tip (ThermoFisher Scientific) were used, respectively. The sample was then dried and resuspended and fractionated at pH 10 using an Agilent PLRP-S 0.5 × 150 mm column (Agilent, Santa Clara, CA, USA) with elution by a gradient of acetonitrile into 33 fractions. The fractions were then dried and resuspended and submitted for LC-MS3 analysis on an Orbitrap Fusion mass spectrometer equipped with an EASY-nLC UHPLC (ThermoFischer Scientific). Peptides from each fraction were eluted into the mass spectrometer over a period of 110 min. Peptide fragmentation and reporter ion detection were performed using simultaneous precursor selection, where the top 10 most abundant MS2 fragments were selected for higher energy collisional dissociation fragmentation (McAlister et al. 2012). MS2 fragmentation was at 30% collision energy and MS3 fragmentation was at 65% collision energy.

Proteome Discoverer 1.4.1.14 was used to extract and search mass spectra against the Uniprot human canonical database (February 2014; 20 264 entries) using Sequest HT with the percolator algorithm to assign confidence to identifications. Because of the large number of spectra, each fraction was searched and quantified separately, and the search results were then combined in Proteome Discoverer. Peptides that were identified with high confidence (1% false discovery rate) were used for subsequent analysis. Proteins that were identified on the basis of those peptides were grouped to find the smallest list of proteins that explains all identified peptides. Reporter ions were quantified relative to the all-sample-pool reporter intensity and the ratios for each fraction were normalized to a median of 1. The resultant proteomics data were imported into GeneSpring v12.6.1 for quantitative analysis. Ratios reflecting relative protein abundance to the all-sample pool were quantile normalized for each of the samples. Statistical significance of alterations in protein level between tuber and nontuber samples was determined using a two-sided student's t-test and the Storey bootstrap false discovery rate method (GeneSpring). Fisher's exact test was used to calculate significance of enrichment for repressed target proteins (JMP 11).

For analysis of p53 pathway activation, we used a published list of genes with multiple levels of evidence demonstrating direct induction by p53 transcriptional regulation (Riley et al. 2008). Of the 136 genes annotated with p53 activator sites in Supplementary Table 2 of Riley et al., we found 28 with proteomics spectra for all samples in our dataset. These were subsequently used to assess p53 activity. Likewise, we used a set of direct target genes to measure NF-κB activity. We used the curated list of NF-κB target genes from http://bioinfo.lifl.fr/NF-KB/ and downloaded all genes identified as “human gene with checked binding sites.” Of the 104 genes meeting this criterion, 24 genes had proteomics spectra for all of our TSC samples. Significance of p53 and NF-κB pathway activation was determined by analysis of log2(T/N) values for target proteins, where T represents protein expression in tuber and N is expression in nontuber. The one-sample t-test against zero was used to determine statistical significance (one-sided, JMP 11). Empirical cumulative distributions of the log2(T/N) values from the proteome (5748 proteins), p53-activated proteins (28), and NF-κB target proteins (24) were derived with JMP 11. Statistical significance of the difference between the proteome distribution and each of the p53 and NF-κB distributions was determined using the two-sample Kolmogorov–Smirnov test (two-sided, JMP 11).

Results

Identification of Aberrant microRNA Expression in Cortical Tubers

We performed microRNA expression analysis on matched pairs of epileptogenic tubers (seizure onset) and adjacent nontuber tissue (nononset) for 5 subjects: 2 with TSC1 mutations and 3 with TSC2 mutations (Table 1). Agilent microRNA microarrays having probes for more than 900 human microRNAs were used to quantify microRNA expression for each of the 10 tissue samples. Stringent filtering and statistical analyses (Materials and Methods) were used to arrive at a high-confidence list of differentially expressed microRNAs. A FWER correction was used to ensure that the probability of one or more false positives being among the selected microRNAs was only 5%. This approach identified 5 statistically significant microRNAs having a minimum 2-fold difference between tuber and nontuber tissues (Table 2). Four microRNAs exhibited higher expression in tuber compared with nontuber tissue (miRs-34a, 23a, 34b*, 532-5p), while one microRNA had lower expression (miR-381).

Table 2

Statistically significant microRNAs differentially expressed in TSC tubers

microRNA P P (corrected) Microarray FC qRT-PCR FC Chr miRbase accession 
hsa-miR-34a 0.0012 0.0000 3.04 6.79 MIMAT0000255 
hsa-miR-23a 0.0048 0.0000 2.65 2.80 19 MIMAT0000078 
hsa-miR-532-5p 0.0101 0.0202 2.89 1.86 MIMAT0002888 
hsa-miR-34b* 0.0007 0.0000 3.58  11 MIMAT0000685 
hsa-miR-381 0.0098 0.0101 −2.18  14 MIMAT0000736 
microRNA P P (corrected) Microarray FC qRT-PCR FC Chr miRbase accession 
hsa-miR-34a 0.0012 0.0000 3.04 6.79 MIMAT0000255 
hsa-miR-23a 0.0048 0.0000 2.65 2.80 19 MIMAT0000078 
hsa-miR-532-5p 0.0101 0.0202 2.89 1.86 MIMAT0002888 
hsa-miR-34b* 0.0007 0.0000 3.58  11 MIMAT0000685 
hsa-miR-381 0.0098 0.0101 −2.18  14 MIMAT0000736 

Quantitative RT-PCR Confirms microRNA Overexpression in Cortical Tubers

Computational analysis (below) suggested that miR-34b* targets few transcripts, while miRs 23a, 34a, and 532-5p were predicted to have a combinatorial effect on many genes involved in neurological processes. Therefore, we performed qRT-PCR analysis on the latter group of microRNAs to confirm the microarray results. Expression levels for each microRNA were compared between patient-matched tuber and nontuber tissue and the mean fold change calculated from the 5 patients (Table 2). All 3 microRNAs were confirmed to have higher expression in tubers than the adjacent nontuber tissue, consistent with the microarray results.

Comparison of microRNA Expression in Tubers and Nontuber Tissue to Normal Cortex

Tissue was designated as tuber or nontuber by a neuropathologist, although the nontuber tissue may not be entirely normal. The tissue was adjacent to the seizure-onset zone (tuber) and also exhibited interictal spiking activity on the intracranial EEG. Additionally, both tuber and nontuberal tissues are haploinsufficient for TSC1 or TSC2. We found differential expression of several microRNAs in the matched tissue comparison; however, this approach did not indicate how the expression levels relate to normal cortical tissue from non-TSC subjects. To address that question, we utilized microRNA data from a postmortem (PM) study of primate brain tissue that included samples from human, chimpanzee, and macaque (Somel et al. 2011). The study investigated microRNA expression in prefrontal cortex and cerebellar cortex from subjects representing a wide range of ages. We downloaded the dataset from the NCBI Gene Expression Omnibus repository (GEO ID GSE29356) and utilized the data for prefrontal cortex specimens from 8 human subjects ranging in age from 2 days to 88 years. These data were generated using the same microRNA microarray platform and version that we used in our TSC study, thus facilitating direct comparison to our results. To further ensure compatibility between the 2 datasets, we co-normalized the primate PM data with our TSC data using quantile normalization. This procedure results in matched quantiles of expression level across all microarrays. To determine if the normalization approach was effective, we compared expression levels between the 2 datasets for 51 microRNAs that were invariant in our TSC tuber versus nontuber analysis. These microRNAs had expression coefficients of variations <5%, indicating that they are not differentially expressed in TSC tubers compared with nontuber tissue, and we would therefore expect similar expression levels in the PM controls. Using normalized expression values for the 51 microRNAs, a scatter plot comparison of the PM values versus the TSC data provided an excellent linear correlation, with a Pearson's correlation coefficient = 0.93 and a linear fit of y = 0.997x (data not shown). These metrics indicate that the normalized expression values from the 2 datasets are similar, thus enabling the below comparison of microRNA expression in TSC tissues to their levels in normal PM cortex.

Analysis of the expression level of miR-34a in the PM control samples revealed a pronounced postnatal increase in expression that plateaus after the teenage years. Age-dependent elevation of miR-34a expression in brain and cardiac tissue has previously been reported (Somel et al. 2010; Li et al. 2011; Boon et al. 2013). Since our pediatric subjects are within the timeframe of rapid increase in miR-34a expression, it is imperative that we consider subject age when investigating microRNA expression events in this population. We did not observe age-dependent expression of miRs-23a, 34b*, 532-5p, and 381 in the PM dataset. Figure 1A presents miR-34a expression as a function of age for each of the PM subjects and the TSC samples in our study. Expression levels for PM controls are represented by gray points and are fit with a logarithmic curve (blue). Expression in nontuber tissue from our TSC subjects is indicated by yellow inverted triangles, and these points are generally situated on or near the curve fit to the PM controls, indicating that miR-34a expression in the TSC nontuber tissue is near normal. However, the expression of miR-34a in epileptogenic tubers is clearly elevated above the expected age-associated level for each subject, as indicated by the red diamonds. Expression of miRs-23a, 34b*, and 532-5p in nontuber tissue was similar to levels in the PM controls, and the corresponding expression level in epileptogenic tubers was clearly elevated for each microRNA (Fig. 1B). A measurable difference between PM controls and nontuber tissue was observed for miR-381, with expression of tuber < nontuber < PM (data not shown).

Figure 1.

Expression of 4 microRNAs is significantly elevated in epileptogenic tubers compared with adjacent nontuber tissue and postmortem controls. (A) Expression data from a previously published postmortem study reveals age-dependent expression of miR-34a in human prefrontal cortex (gray points and blue curve). Considering subject age, the expression of miR-34a in TSC nontuber tissue is near normal (yellow triangles), while expression in tubers is elevated (red diamonds). (B) MicroRNA expression levels in TSC nontuber tissue (N, yellow boxes) are similar to postmortem controls (P, black boxes) for miRs-23a, 532-5p, and 34b*. Each microRNA is overexpressed in epileptogenic tubers (T, red boxes) compared with controls.

Figure 1.

Expression of 4 microRNAs is significantly elevated in epileptogenic tubers compared with adjacent nontuber tissue and postmortem controls. (A) Expression data from a previously published postmortem study reveals age-dependent expression of miR-34a in human prefrontal cortex (gray points and blue curve). Considering subject age, the expression of miR-34a in TSC nontuber tissue is near normal (yellow triangles), while expression in tubers is elevated (red diamonds). (B) MicroRNA expression levels in TSC nontuber tissue (N, yellow boxes) are similar to postmortem controls (P, black boxes) for miRs-23a, 532-5p, and 34b*. Each microRNA is overexpressed in epileptogenic tubers (T, red boxes) compared with controls.

Quantitative LC-MS/MS Proteomic Profiling Demonstrates Significant Repression Among Combinatorial Targets and Enrichment for Synaptic Signal Transmission Proteins

We used liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomics with tandem mass tags (TMT) to quantify protein levels in tuber and nontuber tissues. This approach allowed us to: 1) perform quantitative analysis that is superior to traditional Western blots (Aebersold et al. 2013); 2) perform global analysis for a large portion of the proteome; and 3) perform concurrent analysis of all samples (TMT) thus providing an important internal control for the comparison of tuber and nontuber protein levels. One limitation of LC-MS/MS is the requirement for an ample amount of protein. We had sufficient protein from tuber and nontuber from 3 of the 5 patients (E3007, 81603, H2407). Patient 90602 had sufficient protein from nontuber tissue. We were also able to extract protein from an additional tuber from subject 81603. Thus, the proteomics analysis was performed on 8 samples in total: 4 tuber and 4 nontuber tissues. Overall 614 115 MS2 spectra were submitted and 136 208 were identified (22%). About 42 060 peptides and 6309 proteins were identified on the basis of these spectra. We obtained quantifiable mass spectra for 5749 proteins in all 8 samples. The set of 5749 proteins quantified represents somewhat less than half of all the proteins expected to be present in these tissues (Michalski et al. 2011; Nagaraj et al. 2011). Very low abundance proteins and proteins that do not generate tryptic peptides that produce good spectra are unlikely to be identified. We first identified proteins with a significant change in abundance between tuber and nontuber samples. Statistical selection using a 10% false discovery rate and minimum 1.5-fold change resulted in 1745 significant proteins. Of these, 842 proteins were elevated and 903 had lower levels in tubers.

To investigate protein expression altered by the overexpressed microRNAs, we first identified high-confidence target transcripts using a prediction consensus approach. It is widely recognized that computational methods for microRNA target prediction have a high false-positive rate (Yue et al. 2009; Reyes-Herrera and Ficarra 2012). The number of false positives can be reduced by applying a consensus of multiple prediction methods (Kuhn et al. 2008). We used a consensus tool (miRWalk) to identify putative targets for each of the 4 microRNAs overexpressed in tubers (Dweep et al. 2011). A consensus score was defined for each microRNA/transcript interaction as the number of algorithms (of 10) that predicted that the transcript is targeted by the given microRNA. We then focused on the set of transcripts that are targets of more than one of the 4 microRNAs. Since a single microRNA may target many transcripts, and a given transcript 3′ UTR may have target sites for multiple microRNA species, it is important to consider potential combinatorial targeting among a set of differentially expressed microRNAs (Dombkowski et al. 2011). To account for such combined effects a combinatorial consensus score (CCS) was calculated for each transcript by summing the individual consensus score from all 4 of the microRNAs. The target transcripts were ranked using the CCS.

Our proteome profiling allowed us to empirically determine the minimal CCS associated with significant target repression. Since the 4 microRNAs were overexpressed in tubers their activity would be evident by a reduction in target protein levels. Using the set of 1745 differentially expressed proteins, we binned the proteins by CCS and then determined the fraction of proteins in each bin (score) that were repressed in tubers. Considerable enrichment for repressed proteins was observed with targets having a CCS of 13 or greater. We found that 68% (85/125) of proteins having a CCS ≥13 were repressed as compared with 50.5% (818/1620) of proteins with a CCS ≤12 (P = 0.00018) (Fig. 2). The results demonstrate significant repression among proteins having a high-confidence combinatorial target score, evidence of targeting by the 4 overexpressed microRNAs.

Figure 2.

The proportion of repressed proteins among predicted combinatorial targets is significantly greater than in the overall proteome. Using quantitative liquid chromatography-tandem mass spectrometry proteomics, a total of 1745 proteins were identified with significant differences in protein abundance comparing epileptogenic tubers with adjacent nontuber tissue. The proteins were segregated into 2 primary sets: 1) predicted combinatorial targets of the 4 overexpressed microRNAs; and 2) the remaining proteins (proteome). The fraction of repressed proteins in the predicted combinatorial target set is significantly greater than in the overall proteome (P = 0.00018), reflecting the repressive effect of the 4 microRNAs.

Figure 2.

The proportion of repressed proteins among predicted combinatorial targets is significantly greater than in the overall proteome. Using quantitative liquid chromatography-tandem mass spectrometry proteomics, a total of 1745 proteins were identified with significant differences in protein abundance comparing epileptogenic tubers with adjacent nontuber tissue. The proteins were segregated into 2 primary sets: 1) predicted combinatorial targets of the 4 overexpressed microRNAs; and 2) the remaining proteins (proteome). The fraction of repressed proteins in the predicted combinatorial target set is significantly greater than in the overall proteome (P = 0.00018), reflecting the repressive effect of the 4 microRNAs.

We performed a functional analysis of the 85 repressed combinatorial target proteins (CCS ≥13) using DAVID (Database for Annotation, Visualization, and Integrated Discovery) to identify gene ontologies and functional categories associated with the repressed targets (Huang da et al. 2009). Using the gene ontology (GO) biological process, the most significant ontology was “transmission of nerve impulse. ” By definition of the GO Consortium, this ontology is assigned to genes/proteins involved in synaptic signaling and consequent electrochemical polarization/depolarization in neurons. The fraction of repressed target proteins with this ontology (14/85, Table 3) is highly significant (P = 1.6e−8) and represents a 7.8-fold enrichment. The enrichment value indicates that the number of repressed target proteins involved in synaptic signaling is nearly 8 times what is expected by chance, given the number of genes with this ontology found throughout the entire genome. The proportion of proteins in this functional category (16.5%) is markedly higher in the repressed combinatorial targets than in the group of repressed nontarget proteins (5.9%). The results demonstrate that the set of transcripts targeted by the collective group of 4 microRNAs is highly enriched for genes/proteins involved in synaptic signaling. Additionally, 9 genes known to confer risk of epilepsy are among the 85 repressed combinatorial target genes (Table 3), including the TSC1 protein hamartin, synapsin II (SYN2), γ-aminobutyrate (GABA) A receptor beta3 (GABRB3), doublecortin (DCX), and neurofibromin (NF1). The consensus score is shown for each predicted microRNA/target interaction. The scores for miR-34b* are consistently low, suggesting that this microRNA is not significantly involved in the regulation of these transcripts. Conversely, the scores for miR-34a are consistently high indicating that this microRNA has a prominent role in the post-transcriptional regulation of these genes. Other high-ranking and significantly enriched ontologies among the repressed target proteins included “neurological system process” and “neuron development.”

Table 3

Combinatorial target proteins significantly repressed in tubers and associated with synaptic signaling or epilepsy risk 

Gene Description UniProt Accession Protein FC (T/NTrans. nerve impulse Epilepsy risk Prediction score
 
miR-34a miR-23a miR-532-5p miR-34b* 
GABRA3 Gamma-aminobutyric acid receptor subunit alpha-3 P34903 −6.7 ♦  
CACNA1E Voltage-dependent R-type calcium channel subunit alpha-1E Q15878 −5.0 ♦ ▪ 
SCN3B Sodium channel subunit beta-3 Q9NY72 −4.4  ▪ 
GABBR2 Gamma-aminobutyric acid type B receptor subunit 2 O75899 −3.0 ♦ ▪ 
GABRB3 Gamma-aminobutyric acid receptor subunit beta-3 P28472 −2.9 ♦ ▪ 
SLC6A1 Sodium- and chloride-dependent GABA transporter 1 P30531 −2.7 ♦  
SYN2 Synapsin-2 Q92777 −2.7 ♦ ▪ 
DCX Neuronal migration protein doublecortin O43602 −2.6  ▪ 
SLC30A3 Zinc transporter 3 Q99726 −2.4  ▪ 
APBA1 Amyloid beta A4 precursor protein-binding family A member 1 Q02410 −2.3 ♦  
NPTX1 Neuronal pentraxin-1 Q15818 −2.2 ♦  
TSC1 Hamartin Q92574 −2.2 ♦ ▪ 
KCNMA1 Calcium-activated potassium channel subunit alpha-1 Q12791 −1.9 ♦  
PVRL1 Poliovirus receptor-related protein 1 Q15223 −1.9 ♦  
SNPH Syntaphilin O15079 −1.7 ♦  
NF1 Neurofibromin P21359 −1.6 ♦ ▪ 
KIF1B Kinesin-like protein KIF1B O60333 −1.6 ♦  
Gene Description UniProt Accession Protein FC (T/NTrans. nerve impulse Epilepsy risk Prediction score
 
miR-34a miR-23a miR-532-5p miR-34b* 
GABRA3 Gamma-aminobutyric acid receptor subunit alpha-3 P34903 −6.7 ♦  
CACNA1E Voltage-dependent R-type calcium channel subunit alpha-1E Q15878 −5.0 ♦ ▪ 
SCN3B Sodium channel subunit beta-3 Q9NY72 −4.4  ▪ 
GABBR2 Gamma-aminobutyric acid type B receptor subunit 2 O75899 −3.0 ♦ ▪ 
GABRB3 Gamma-aminobutyric acid receptor subunit beta-3 P28472 −2.9 ♦ ▪ 
SLC6A1 Sodium- and chloride-dependent GABA transporter 1 P30531 −2.7 ♦  
SYN2 Synapsin-2 Q92777 −2.7 ♦ ▪ 
DCX Neuronal migration protein doublecortin O43602 −2.6  ▪ 
SLC30A3 Zinc transporter 3 Q99726 −2.4  ▪ 
APBA1 Amyloid beta A4 precursor protein-binding family A member 1 Q02410 −2.3 ♦  
NPTX1 Neuronal pentraxin-1 Q15818 −2.2 ♦  
TSC1 Hamartin Q92574 −2.2 ♦ ▪ 
KCNMA1 Calcium-activated potassium channel subunit alpha-1 Q12791 −1.9 ♦  
PVRL1 Poliovirus receptor-related protein 1 Q15223 −1.9 ♦  
SNPH Syntaphilin O15079 −1.7 ♦  
NF1 Neurofibromin P21359 −1.6 ♦ ▪ 
KIF1B Kinesin-like protein KIF1B O60333 −1.6 ♦  

Note: Shading corresponds to the consensus prediction score.

Hamartin is Repressed in Tubers

A notable repressed protein among the combinatorial targets is hamartin, the protein product of the TSC1 gene. The quantitative proteomics data show that, on average, hamartin levels were 2.2-fold lower in epileptogenic tubers compared with nontuber tissue (Fig. 3). Interestingly, hamartin levels were decreased in tubers of the TSC1 patient and the TSC2 patients. TSC1 is predicted to be a target of miRs-23a, 34a, and 532-5p based on a consensus of 5, 4, and 5 prediction algorithms, respectively (Table 3).

Figure 3.

Hamartin is significantly reduced in epileptogenic tubers compared with perituberal tissue. Among the differentially expressed proteins identified in our quantitative LC-MS/MS proteomics experiment, hamartin was repressed 2.2-fold in tubers (T) compared with nontuber tissue (N). Reduced levels were observed in subjects having mutations in either TSC1 or TSC2 genes.

Figure 3.

Hamartin is significantly reduced in epileptogenic tubers compared with perituberal tissue. Among the differentially expressed proteins identified in our quantitative LC-MS/MS proteomics experiment, hamartin was repressed 2.2-fold in tubers (T) compared with nontuber tissue (N). Reduced levels were observed in subjects having mutations in either TSC1 or TSC2 genes.

Luciferase Reporter Assays Confirm Predicted microRNA/Target Interactions of TSC1

We utilized a luciferase reporter assay to validate targeting of the 3′ UTR of TSC1 by miRs-23a and 34a. A vector construct created with the firefly luciferase reporter gene and the 3′UTR of TSC1 was transfected into COS-7 cells. This cell line has been widely used for microRNA target validation, including studies of neural development (Peng et al. 2012; Zhao et al. 2012). Importantly, we used a construct that included the entire TSC1 3′ UTR since truncated UTRs may alter microRNA/UTR interactions (Kuhn et al. 2008). Co-transfection with a vector expressing pre-miR-34a resulted in a 71% decrease in expression of the luciferase reporter compared with control expression vector (P = 0.0005) (Fig. 4). A 40% decrease in reporter expression was observed when co-transfecting with pre-miR-23a compared with control expression vector transfection.

Figure 4.

Luciferase reporter assays confirm that miRs-34a and 23a target the 3′ UTR of TSC1. A luciferase reporter construct with the full-length 3′ UTR of TSC1 was co-transfected into COS-7 cells with pre-miR expression vector or control vector. Co-transfection with pre-miR-34a resulted in a 71% reduction in reporter expression, and miR-23a resulted in a 40% reduction.

Figure 4.

Luciferase reporter assays confirm that miRs-34a and 23a target the 3′ UTR of TSC1. A luciferase reporter construct with the full-length 3′ UTR of TSC1 was co-transfected into COS-7 cells with pre-miR expression vector or control vector. Co-transfection with pre-miR-34a resulted in a 71% reduction in reporter expression, and miR-23a resulted in a 40% reduction.

Quantitative LC-MS/MS Proteomic Profiling Demonstrates Activation of p53 and Inflammatory Signaling in Epileptogenic Tubers

To investigate the potential role of p53 and proinflammatory cytokine signaling in dysregulation of microRNAs in tubers, we characterized expression profiles for proteins that are direct targets of p53 and NF-κB. While spectra for p53 and NF-κB themselves were not available in the proteomics experiment, their abundance alone would not be an accurate determinant of activity since both proteins are also regulated through phosphorylation. Therefore, our analysis of direct transcriptional targets provides a way to view pathway activity.

To assess p53 pathway activation, we used 28 proteins known to be directly induced by p53 through transcriptional regulation (Materials and Methods). For each of these proteins, we calculated log2(T/N) using the average expression value for tuber (T) and nontuber (N). This parameter is zero when no change in expression is measured, negative when repressed in tubers, and positive when overexpressed in tubers compared with nontuber. This metric was also calculated for all 5749 proteins having proteomics spectra for all samples. We refer to this set as the “proteome.” Expression levels for the group of 28 p53-activated proteins were significantly elevated (P = 0.015), with mean log2(T/N) = 0.35 (1.27-fold). Mean log2(T/N) for the proteome was 0.0029, very close to the expected value of zero.

We performed the same analysis for a group of 24 proteins established as direct targets of NF-κB, a key mediator of inflammation (Materials and Methods). We found that expression levels of the overall set of NF-κB target proteins to be significantly elevated in tubers (P < 0.0001), with mean log2(T/N) = 0.96 (1.94-fold). The distributions of log2(T/N) values for the p53- and NF-κB-regulated proteins were significantly different than the overall proteome and reflect overall induction of the proteins. These results are evident in the empirical cumulative distributions shown in Figure 5. Cumulative distributions provide the probability (y-axis) of observing a specified value or less (x-axis). The cumulative distribution of log2(T/N) values for the proteome is shown by the red, solid curve. Distributions for p53- and NF-κB-regulated proteins are clearly shifted to the right, reflecting increased expression in tubers for proteins regulated by the 2 transcription factors. Comparison of each distribution to the overall proteome reveals statistical significance, P = 0.0112 and 0.0005, respectively, for p53 and NF-κB target sets. Tables with proteomics data for p53 and NF-κB target proteins are found in the supplementary files.

Figure 5.

Proteins regulated by p53 and NF-κB are overexpressed in tubers compared with nontuber tissue. Empirical cumulative distributions of log2(T/N) are shown for three groups of proteins, where T and N are average protein expression in tuber and nontuber tissue, respectively. The distribution for the overall proteome (5748 proteins) is shown with the red, solid line, the distribution for proteins regulated by p53 (28) is shown by the dashed gray line, and the distribution for proteins regulated by NF-κB (24) is shown by the dashed blue line. The y-axis provides the probability that the specified value, or less, of log2(T/N) will be observed. The proteome distribution is centered on zero reflecting equal probability of induced or repressed proteins. The p53 and NF-κB target sets are shifted to the right reflecting protein overexpression in tubers.

Figure 5.

Proteins regulated by p53 and NF-κB are overexpressed in tubers compared with nontuber tissue. Empirical cumulative distributions of log2(T/N) are shown for three groups of proteins, where T and N are average protein expression in tuber and nontuber tissue, respectively. The distribution for the overall proteome (5748 proteins) is shown with the red, solid line, the distribution for proteins regulated by p53 (28) is shown by the dashed gray line, and the distribution for proteins regulated by NF-κB (24) is shown by the dashed blue line. The y-axis provides the probability that the specified value, or less, of log2(T/N) will be observed. The proteome distribution is centered on zero reflecting equal probability of induced or repressed proteins. The p53 and NF-κB target sets are shifted to the right reflecting protein overexpression in tubers.

Discussion

While microRNAs are enriched in the brain and implicated in many diseases, the role of microRNAs in brain from TSC patients has previously not been investigated. We report aberrant microRNA expression in tubers resected from TSC1 and TSC2 patients to treat medically intractable seizures. Comparing expression profiles in subject-matched pairs of tubers and adjacent nontuber tissue, we identified 5 differentially expressed microRNAs, 4 of which were overexpressed. Our integration of a previously published microRNA expression dataset from PM brain tissue provided control measurements. We found that expression levels of the 4 microRNAs in nontuber cortical tissue were near normal, and expression in epileptogenic tubers was higher than in the PM controls. The analysis also revealed a pronounced age-dependent expression pattern for miR-34a that highlights the need for age- or patient-matched controls.

While miR-34a has been reported as a tumor suppressor with proapoptotic activity, a growing body of literature has shown miR-34a to be involved in neurodevelopment and cortical neurogenesis (Aranha et al. 2010, 2011; Fineberg et al. 2012). Overexpression of miR-34a has been shown to target synaptic genes and reduce the number of inhibitory synapses (Agostini et al. 2011). A recent study of neural stem cell differentiation demonstrated that overexpression of miR-34a resulted in significantly impaired neuronal differentiation and synapse function (Morgado et al. 2014). The authors stated that downregulation of miR-34a seems to be “crucial for neurogenesis progression.” That conclusion is consistent with low levels of miR-34a during the postnatal period, as we observed in the PM dataset. Overexpression of miR-34a during this critical period, as well as in utero, would be expected to disrupt normal neurodevelopment.

Several groups have reported increased levels of miR-34a in animal models of epilepsy (Hu et al. 2011, 2012; Sano et al. 2012; Risbud and Porter 2013). MiRs 23a and 381 have also been reported as differentially expressed in epilepsy, and we found these microRNAs differentially expressed in epileptogenic tubers as well (Song et al. 2011; Hu et al. 2012; Risbud and Porter 2013). Dysregulation of miR-34a has been associated with a range of neurological disorders. An analysis of genomic copy-number variants (CNVs) associated with autism identified miR-34a as one of 10 “hub” microRNAs (microRNAs targeting multiple autism risk genes) resident in the autism CNV regions (Vaishnavi et al. 2013). MiR-34a has also been identified as a potential blood-based biomarker of schizophrenia (Lai et al. 2011). In a case/control comparison, miR-34a was found to be 2.5-fold higher in mononuclear leukocytes of schizophrenia patients. This observation is consistent with a PM study that found miR-34a overexpression in the prefrontal cortex of patients with schizophrenia (Kim et al. 2010). Overexpression of miR-34a was also reported in the cerebral cortex of a mouse model of Alzheimer's disease (Wang et al. 2009).

The endpoint of microRNA activity is best observed at the protein level since many microRNA/transcript interactions impede translation and may not alter transcript levels (Selbach et al. 2008; Li et al. 2012; Liu et al. 2013). We used quantitative LC-MS/MS proteomic profiling to investigate alterations in protein abundance for targets of the 4 overexpressed microRNAs. We demonstrated significant repression in tubers for proteins of transcripts with a high-confidence combinatorial target score. The set of repressed target transcripts was highly enriched for genes involved in synaptic signaling. Additionally, the proteins for a number of genes known to confer risk of epilepsy were among the combinatorial targets repressed in tubers. Notably, among the repressed target proteins associated with epilepsy risk and synaptic signal transmission was hamartin, the product of the TSC1 gene. We used a luciferase reporter assay to demonstrate targeting of the TSC1 3′ UTR by miRs-34a and 23a. This finding is consistent with a recent publication, and our own computational analysis, that predict targeting of TSC1 by miR-23a (Romaker et al. 2014). We also found decreased hamartin in tubers from the TSC1 patient, as well as those with TSC2 mutations.

Investigations from a number of laboratories suggest the TSC genes may be post-transcriptionally regulated. A number of studies have reported repressed hamartin and/or tuberin levels in cortical tubers, even where loss of heterozygosity (LOH) or other second-hit mutations of the genes could not be identified (Kerfoot et al. 1996; Mizuguchi et al. 1996, 1997, 2000; Mizuguchi and Takashima 2001; Vinaitheerthan et al. 2004; Boer et al. 2008). While reduced expression of one of the proteins could be expected in the presence of inactivating mutations in the corresponding gene (e.g., truncations), many of the reports noted simultaneous repression of hamartin and tuberin with no clear causative explanation. It has been speculated that mutation of one of the TSC genes may reduce expression of the other gene product, but no mechanism has been uncovered (Jozwiak et al. 2004). Niida et al. (2001) noted that one potential explanation is that “expression of the wild-type protein being ‘turned off’ or reduced as a result of epigenetic events,” yet they found no evidence of methylation-based gene silencing. Collectively, these observations describe hallmarks of microRNA activity. We have found aberrant microRNA expression in cortical tubers and their targeting of TSC1. Subsequent mechanistic studies are warranted to determine if the aberrant microRNAs represent a type of “second hit,” contributing to tuber formation in the absence of LOH (Crino et al. 2010) or if their dysregulation is an outcome of tuber pathology.

In addition to TSC1, our integrated microRNA/proteomic profiling revealed a number of target transcripts which are known to cause epilepsy when deficient or deleted (Table 3). Knockout of SYN2 was shown to cause seizures in a mouse model (Rosahl et al. 1995). Mice with inactivated GABRB3 have a seizure phenotype (Homanics et al. 1997). Mutations in DCX result in type I lissencephaly, with clinical features of disorganized cortical layering, epilepsy, and mental retardation (Gleeson et al. 1998). Mutations in NF1 lead to neurofibromatosis type I, a neurocutaneous syndrome characterized by benign brain lesions, cognitive disorders, and seizures in ∼10% of patients (Ostendorf et al. 2013). Each of these genes is a high-confidence combinatorial target of the 4 overexpressed microRNAs, and their protein levels were significantly decreased in tubers.

A model for the mechanism of microRNA dysregulation and their role in TSC tuber pathology emerges from our findings and the current literature (Fig. 6). Considerable evidence in the literature has shown that: 1) loss of TSC1 or TSC2 results in activation of p53; and 2) activated p53 induces miRs-34a and 23a. Using cell line knockouts of TSC1 and TSC2, a study confirmed that “loss of TSC1 or TSC2 results in a dramatic elevation of p53 protein” due to increased translation stimulated by mTOR activation (Lee et al. 2007). The same study reported elevated p53 levels in human angiomyolipomas due to TSC mutations. The authors presented an intriguing hypothesis that the proapoptotic effect of p53 might be the reason why TSC lesions are generally benign. A recent study found that, in addition to increased synthesis of p53, mTOR activates p53 through phosphorylation of Ser46 (Krzesniak et al. 2014). Evidence of elevated p53 activity in TSC tubers is evident in our proteomics dataset. We analyzed expression levels for the proteins produced from a set of known p53-activated genes. The overall set of proteins was significantly increased in tubers compared with nontuber tissue, consistent with p53 activation. A number of reports have demonstrated the direct transactivation of miR-34a by p53, and this regulatory axis has gained substantial interest (Chang et al. 2007; Raver-Shapira et al. 2007; Rokavec et al. 2014). Induction of both miR-34a and p53 due to loss of TSC1 was reported in a study detailing the role of TSC1 in the survival of mast cells (Shin et al. 2012). MiR-34b* has also been shown to be activated by p53 (He et al. 2007), and a comparison of p53 null and wild-type colon cancer cell lines identified miR-23a as a probable target of p53 (Chang et al. 2007). Recently, 2 studies have confirmed that miR-23a is induced by p53 in hepatocellular carcinoma (Wang et al. 2013, 2014). Collectively, these reports and our results indicate that p53 activation is a likely mechanism involved in aberrant expression of microRNAs with loss of TSC1 or TSC2 in cortical tubers, and downstream microRNA targeting may contribute to tuber pathogenesis through repression of genes involved in neuronal development and synaptic signaling, as well as epilepsy risk genes.

Figure 6.

Mechanistic model depicting roles of microRNAs and signaling pathways in TSC tuber pathogenesis and epileptogenesis. Previous reports have shown that mammalian/mechanistic target of rapamycin activation due to TSC mutations results in induction of miRs-23a and 34a mediated through p53. We have shown that these microRNAs are overexpressed in epileptogenic tubers, along with miR-532-5p, and they target transcripts involved in synaptic signaling, neuron development, and a number of genes known to confer risk of epilepsy. The repression of target proteins may contribute to tuber pathogenesis as denoted by MRI showing the locations of multiple tubers. Events leading to inflammation in a subset of tubers may further exacerbate overexpression of the microRNAs mediated by proinflammatory cytokines. The combined effects of these mechanisms may lead to epileptogenesis in a subset of tubers, as highlighted by the MRI and AMT-PET imaging shown in the figure. The red arrow indicates an epileptogenic tuber with high AMT uptake, a marker of inflammation. The remaining arrows note the locations of nonepileptogenic tubers.

Figure 6.

Mechanistic model depicting roles of microRNAs and signaling pathways in TSC tuber pathogenesis and epileptogenesis. Previous reports have shown that mammalian/mechanistic target of rapamycin activation due to TSC mutations results in induction of miRs-23a and 34a mediated through p53. We have shown that these microRNAs are overexpressed in epileptogenic tubers, along with miR-532-5p, and they target transcripts involved in synaptic signaling, neuron development, and a number of genes known to confer risk of epilepsy. The repression of target proteins may contribute to tuber pathogenesis as denoted by MRI showing the locations of multiple tubers. Events leading to inflammation in a subset of tubers may further exacerbate overexpression of the microRNAs mediated by proinflammatory cytokines. The combined effects of these mechanisms may lead to epileptogenesis in a subset of tubers, as highlighted by the MRI and AMT-PET imaging shown in the figure. The red arrow indicates an epileptogenic tuber with high AMT uptake, a marker of inflammation. The remaining arrows note the locations of nonepileptogenic tubers.

Inflammation and proinflammatory cytokines may also be mechanistically involved in microRNA regulation in epileptogenic tubers. Focal epilepsy produces an inflammatory response (Aronica and Crino 2011), and the role of microRNAs in neuroinflammation has recently drawn much attention (Thounaojam et al. 2013). Numerous microRNAs have been reported as dysregulated in a variety of neurodegenerative disorders associated with inflammation, including Alzheimer's disease, Huntington's disease, multiple sclerosis, and Parkinson disease. Similar to our finding in epileptogenic TSC tubers, expression levels of microRNAs 34a and 23a are both increased in active lesions of multiple sclerosis patients (Junker et al. 2009). The study also reported that the 2 miRs are induced in response to proinflammatory cytokines interferon gamma, interleukin 1, and tumor necrosis factor (TNFα) in cultured astrocytes. Among these cytokines, TNFα produced the greatest increase in expression. Proinflammatory cytokine signaling along with elevated TNFα and NF-κB expression has been reported in TSC tubers (Maldonado et al. 2003). Additionally, a microarray gene expression analysis of surgically resected tubers found induction of many genes involved in inflammation (Boer et al. 2010). Consistent with these reports, our proteomics data provides evidence of proinflammatory signaling in epileptogenic tubers. We analyzed the abundance of a group of proteins produced from genes transcriptionally regulated by NF-κB, a transcription factor that is a key mediator of the inflammatory response (Tak and Firestein 2001). The overall set of proteins was significantly increased in tubers compared with nontuber tissue, showing considerable NF-κB activation. Notable among this set is ICAM1 (Supplementary Table 2), previously identified as a marker of inflammation in TSC tubers (Maldonado et al. 2003). ICAM1 protein was 3.7-fold higher in tubers than in nontuber tissue. The overall set of NF-κB target genes were induced nearly 2-fold, on average. Our results, along with evidence from earlier reports, suggest that proinflammatory cytokines in tubers may contribute to the increased expression of the aberrant microRNAs.

In summary, epileptogenic tubers from patients with either TSC1 or TCS2 mutations show coordinate upregulation of several microRNAs. Quantitative LC-MS/MS proteomic profiling demonstrated significant repression of predicted combinatorial targets of the overexpressed microRNAs. The repressed targets are enriched for transcripts involved in synaptic signaling and include a number of genes known to confer risk of epilepsy, including TSC1. Luciferase reporter assays demonstrated that miRs-23a and 34a target the 3′ UTR of TSC1. Based on these results and previous reports, we propose a model of the role of the aberrant microRNAs in tuber pathology. The evidence indicate that the microRNAs are induced downstream of mTOR activation, mediated through p53. An open question is whether similar microRNA expression patterns are found in nonepileptogenic tubers. It is a challenging question to address since such tissue is infrequently resected during epilepsy surgery and would require PM analysis. However, given the changes observed in pathology of tubers, it seems likely that microRNAs are involved in alterations of synaptic density and loss of neuronal elements compared with normal cortex. Additionally, inflammatory cytokines present in epileptogenic tubers likely contribute to the elevated microRNA expression and may be more highly expressed in epileptogenic tubers. Aberrant microRNA expression due to TSC1 or TSC2 mutations and proinflammatory events may contribute to tuber pathogenesis and recurrent seizures, and additional mechanistic studies are warranted. Why the observed changes in microRNA expression occur in epileptogenic tubers and not in adjacent histologically normal cortex in the presence of haploinsufficiency for TSC1 or TSC2 remains to be explained.

Supplementary Material

Supplementary material can be found at: http://www.cercor.oxfordjournals.org/.

Funding

This work was supported by the U.S. National Institutes of Health [1R01NS079429-01A1 (A.D.), 5R01NS064989-03 (H.C.), S10OD010700 (P.S.), Center grants P30ES06639 and P30CA022453], and the Epilepsy Foundation (grant #160313 to C.B.).

Notes

We thank S. Sundaram for helpful advice during the course of this research and E. Asano for assistance with EEG designation of epileptogenic tubers. Conflict of Interest: None declared .

References

Aebersold
R
Burlingame
AL
Bradshaw
RA
.
2013
.
Western blots versus selected reaction monitoring assays: time to turn the tables?
Mol Cell Proteomics
 .
12
:
2381
2382
.
Agostini
M
Tucci
P
Steinert
JR
Shalom-Feuerstein
R
Rouleau
M
Aberdam
D
Forsythe
ID
Young
KW
Ventura
A
Concepcion
CP
et al
2011
.
microRNA-34a regulates neurite outgrowth, spinal morphology, and function
.
Proc Natl Acad Sci USA
 .
108
:
21099
21104
.
Aranha
MM
Santos
DM
Sola
S
Steer
CJ
Rodrigues
CM
.
2011
.
miR-34a regulates mouse neural stem cell differentiation
.
PLoS One
 .
6
:
e21396
.
Aranha
MM
Santos
DM
Xavier
JM
Low
WC
Steer
CJ
Sola
S
Rodrigues
CM
.
2010
.
Apoptosis-associated microRNAs are modulated in mouse, rat and human neural differentiation
.
BMC Genomics
 .
11
:
514
.
Aronica
E
Crino
PB
.
2011
.
Inflammation in epilepsy: clinical observations
.
Epilepsia
 .
52
(Suppl 3)
:
26
32
.
Boer
K
Crino
PB
Gorter
JA
Nellist
M
Jansen
FE
Spliet
WG
van Rijen
PC
Wittink
FR
Breit
TM
Troost
D
et al
2010
.
Gene expression analysis of tuberous sclerosis complex cortical tubers reveals increased expression of adhesion and inflammatory factors
.
Brain Pathol
 .
20
:
704
719
.
Boer
K
Troost
D
Jansen
F
Nellist
M
van den Ouweland
AM
Geurts
JJ
Spliet
WG
Crino
P
Aronica
E
.
2008
.
Clinicopathological and immunohistochemical findings in an autopsy case of tuberous sclerosis complex
.
Neuropathology
 .
28
:
577
590
.
Boon
RA
Iekushi
K
Lechner
S
Seeger
T
Fischer
A
Heydt
S
Kaluza
D
Treguer
K
Carmona
G
Bonauer
A
et al
2013
.
MicroRNA-34a regulates cardiac ageing and function
.
Nature
 .
495
:
107
110
.
Chang
TC
Wentzel
EA
Kent
OA
Ramachandran
K
Mullendore
M
Lee
KH
Feldmann
G
Yamakuchi
M
Ferlito
M
Lowenstein
CJ
et al
2007
.
Transactivation of miR-34a by p53 broadly influences gene expression and promotes apoptosis
.
Mol Cell
 .
26
:
745
752
.
Chugani
HT
Luat
AF
Kumar
A
Govindan
R
Pawlik
K
Asano
E
.
2013
.
alpha-[11C]-Methyl-L-tryptophan—PET in 191 patients with tuberous sclerosis complex
.
Neurology
 .
81
:
674
680
.
Crino
PB
Aronica
E
Baltuch
G
Nathanson
KL
.
2010
.
Biallelic TSC gene inactivation in tuberous sclerosis complex
.
Neurology
 .
74
:
1716
1723
.
Dombkowski
AA
Sultana
Z
Craig
DB
Jamil
H
.
2011
.
In silico analysis of combinatorial microRNA activity reveals target genes and pathways associated with breast cancer metastasis
.
Cancer Inform
 .
10
:
13
29
.
Dweep
H
Sticht
C
Pandey
P
Gretz
N
.
2011
.
miRWalk—database: prediction of possible miRNA binding sites by "walking" the genes of three genomes
.
J Biomed Inform
 .
44
:
839
847
.
Filipowicz
W
Bhattacharyya
SN
Sonenberg
N
.
2008
.
Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight?
Nat Rev Genet
 .
9
:
102
114
.
Fineberg
SK
Datta
P
Stein
CS
Davidson
BL
.
2012
.
MiR-34a represses Numbl in murine neural progenitor cells and antagonizes neuronal differentiation
.
PLoS One
 .
7
:
e38562
.
Fineberg
SK
Kosik
KS
Davidson
BL
.
2009
.
MicroRNAs potentiate neural development
.
Neuron
 .
64
:
303
309
.
Gao
J
Liu
QG
.
2011
.
The role of miR-26 in tumors and normal tissues (Review)
.
Oncol Lett
 .
2
:
1019
1023
.
Gleeson
JG
Allen
KM
Fox
JW
Lamperti
ED
Berkovic
S
Scheffer
I
Cooper
EC
Dobyns
WB
Minnerath
SR
Ross
ME
et al
1998
.
Doublecortin, a brain-specific gene mutated in human X-linked lissencephaly and double cortex syndrome, encodes a putative signaling protein
.
Cell
 .
92
:
63
72
.
Hackstadt
AJ
Hess
AM
.
2009
.
Filtering for increased power for microarray data analysis
.
BMC Bioinformatics
 .
10
:
11
.
He
L
He
X
Lim
LP
de Stanchina
E
Xuan
Z
Liang
Y
Xue
W
Zender
L
Magnus
J
Ridzon
D
et al
2007
.
A microRNA component of the p53 tumour suppressor network
.
Nature
 .
447
:
1130
1134
.
Homanics
GE
DeLorey
TM
Firestone
LL
Quinlan
JJ
Handforth
A
Harrison
NL
Krasowski
MD
Rick
CE
Korpi
ER
Makela
R
et al
1997
.
Mice devoid of gamma-aminobutyrate type A receptor beta3 subunit have epilepsy, cleft palate, and hypersensitive behavior
.
Proc Natl Acad Sci USA
 .
94
:
4143
4148
.
Hu
K
Xie
YY
Zhang
C
Ouyang
DS
Long
HY
Sun
DN
Long
LL
Feng
L
Li
Y
Xiao
B
.
2012
.
MicroRNA expression profile of the hippocampus in a rat model of temporal lobe epilepsy and miR-34a-targeted neuroprotection against hippocampal neurone cell apoptosis post-status epilepticus
.
BMC Neurosci
 .
13
:
115
.
Hu
K
Zhang
C
Long
L
Long
X
Feng
L
Li
Y
Xiao
B
.
2011
.
Expression profile of microRNAs in rat hippocampus following lithium-pilocarpine-induced status epilepticus
.
Neurosci Lett
 .
488
:
252
257
.
Huang da
W
Sherman
BT
Lempicki
RA
.
2009
.
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
.
Nat Protoc
 .
4
:
44
57
.
Jimenez-Mateos
EM
Engel
T
Merino-Serrais
P
McKiernan
RC
Tanaka
K
Mouri
G
Sano
T
O'Tuathaigh
C
Waddington
JL
Prenter
S
et al
2012
.
Silencing microRNA-134 produces neuroprotective and prolonged seizure-suppressive effects
.
Nat Med
 .
18
:
1087
1094
.
Jozwiak
S
Kwiatkowski
D
Kotulska
K
Larysz-Brysz
M
Lewin-Kowalik
J
Grajkowska
W
Roszkowski
M
.
2004
.
Tuberin and hamartin expression is reduced in the majority of subependymal giant cell astrocytomas in tuberous sclerosis complex consistent with a two-hit model of pathogenesis
.
J Child Neurol
 .
19
:
102
106
.
Junker
A
Krumbholz
M
Eisele
S
Mohan
H
Augstein
F
Bittner
R
Lassmann
H
Wekerle
H
Hohlfeld
R
Meinl
E
.
2009
.
MicroRNA profiling of multiple sclerosis lesions identifies modulators of the regulatory protein CD47
.
Brain
 .
132
:
3342
3352
.
Kerfoot
C
Wienecke
R
Menchine
M
Emelin
J
Maize
JC
Jr
Welsh
CT
Norman
MG
DeClue
JE
Vinters
HV
.
1996
.
Localization of tuberous sclerosis 2 mRNA and its protein product tuberin in normal human brain and in cerebral lesions of patients with tuberous sclerosis
.
Brain Pathol
 .
6
:
367
375
.
Kim
AH
Reimers
M
Maher
B
Williamson
V
McMichael
O
McClay
JL
van den Oord
EJ
Riley
BP
Kendler
KS
Vladimirov
VI
.
2010
.
MicroRNA expression profiling in the prefrontal cortex of individuals affected with schizophrenia and bipolar disorders
.
Schizophrenia Res
 .
124
:
183
191
.
Krichevsky
AM
King
KS
Donahue
CP
Khrapko
K
Kosik
KS
.
2003
.
A microRNA array reveals extensive regulation of microRNAs during brain development
.
RNA
 .
9
:
1274
1281
.
Krzesniak
M
Zajkowicz
A
Matuszczyk
I
Rusin
M
.
2014
.
Rapamycin prevents strong phosphorylation of p53 on serine 46 and attenuates activation of the p53 pathway in A549 lung cancer cells exposed to actinomycin D
.
Mech Ageing Dev
 .
139
:
11
21
.
Kuhn
DE
Martin
MM
Feldman
DS
Terry
AV
Jr
Nuovo
GJ
Elton
TS
.
2008
.
Experimental validation of miRNA targets
.
Methods
 .
44
:
47
54
.
Lai
CY
Yu
SL
Hsieh
MH
Chen
CH
Chen
HY
Wen
CC
Huang
YH
Hsiao
PC
Hsiao
CK
Liu
CM
et al
2011
.
MicroRNA expression aberration as potential peripheral blood biomarkers for schizophrenia
.
PLoS One
 .
6
:
e21635
.
Lee
CH
Inoki
K
Karbowniczek
M
Petroulakis
E
Sonenberg
N
Henske
EP
Guan
KL
.
2007
.
Constitutive mTOR activation in TSC mutants sensitizes cells to energy starvation and genomic damage via p53
.
EMBO J
 .
26
:
4812
4823
.
Li
C
Xiong
Q
Zhang
J
Ge
F
Bi
LJ
.
2012
.
Quantitative proteomic strategies for the identification of microRNA targets
.
Expert Rev Proteomics
 .
9
:
549
559
.
Li
X
Khanna
A
Li
N
Wang
E
.
2011
.
Circulatory miR34a as an RNA based, noninvasive biomarker for brain aging
.
Aging
 .
3
:
985
1002
.
Liu
Q
Halvey
PJ
Shyr
Y
Slebos
RJ
Liebler
DC
Zhang
B
.
2013
.
Integrative omics analysis reveals the importance and scope of translational repression in microRNA-mediated regulation
.
Mol Cell Proteomics
 .
12
:
1900
1911
.
Maldonado
M
Baybis
M
Newman
D
Kolson
DL
Chen
W
McKhann
G
2nd
Gutmann
DH
Crino
PB
.
2003
.
Expression of ICAM-1, TNF-alpha, NF kappa B, and MAP kinase in tubers of the tuberous sclerosis complex
.
Neurobiol Dis
 .
14
:
279
290
.
McAlister
GC
Huttlin
EL
Haas
W
Ting
L
Jedrychowski
MP
Rogers
JC
Kuhn
K
Pike
I
Grothe
RA
Blethrow
JD
et al
2012
.
Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses
.
Anal Chem
 .
84
:
7469
7478
.
Michalski
A
Cox
J
Mann
M
.
2011
.
More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS
.
J Proteome Res
 .
10
:
1785
1793
.
Mizuguchi
M
Ikeda
K
Takashima
S
.
2000
.
Simultaneous loss of hamartin and tuberin from the cerebrum, kidney and heart with tuberous sclerosis
.
Acta Neuropathol
 .
99
:
503
510
.
Mizuguchi
M
Kato
M
Yamanouchi
H
Ikeda
K
Takashima
S
.
1996
.
Loss of tuberin from cerebral tissues with tuberous sclerosis and astrocytoma
.
Ann Neurol
 .
40
:
941
944
.
Mizuguchi
M
Kato
M
Yamanouchi
H
Ikeda
K
Takashima
S
.
1997
.
Tuberin immunohistochemistry in brain, kidneys and heart with or without tuberous sclerosis
.
Acta Neuropathol
 .
94
:
525
531
.
Mizuguchi
M
Takashima
S
.
2001
.
Neuropathology of tuberous sclerosis
.
Brain Dev
 .
23
:
508
515
.
Morgado
AL
Xavier
JM
Dionisio
PA
Ribeiro
MF
Dias
RB
Sebastiao
AM
Sola
S
Rodrigues
CM
.
2014
.
MicroRNA-34a modulates neural stem cell differentiation by regulating expression of synaptic and autophagic proteins
.
Mol Neurobiol
 .
Nagaraj
N
Wisniewski
JR
Geiger
T
Cox
J
Kircher
M
Kelso
J
Paabo
S
Mann
M
.
2011
.
Deep proteome and transcriptome mapping of a human cancer cell line
.
Mol Syst Biol
 .
7
:
548
.
Nielsen
JA
Lau
P
Maric
D
Barker
JL
Hudson
LD
.
2009
.
Integrating microRNA and mRNA expression profiles of neuronal progenitors to identify regulatory networks underlying the onset of cortical neurogenesis
.
BMC Neurosci
 .
10
:
98
.
Niida
Y
Stemmer-Rachamimov
AO
Logrip
M
Tapon
D
Perez
R
Kwiatkowski
DJ
Sims
K
MacCollin
M
Louis
DN
Ramesh
V
.
2001
.
Survey of somatic mutations in tuberous sclerosis complex (TSC) hamartomas suggests different genetic mechanisms for pathogenesis of TSC lesions
.
Am J Hum Genet
 .
69
:
493
503
.
Ostendorf
AP
Gutmann
DH
Weisenberg
JL
.
2013
.
Epilepsy in individuals with neurofibromatosis type 1
.
Epilepsia
 .
54
:
1810
1814
.
Peng
C
Li
N
Ng
YK
Zhang
J
Meier
F
Theis
FJ
Merkenschlager
M
Chen
W
Wurst
W
Prakash
N
.
2012
.
A unilateral negative feedback loop between miR-200 microRNAs and Sox2/E2F3 controls neural progenitor cell-cycle exit and differentiation
.
J Neurosci
 .
32
:
13292
13308
.
Rao
Y
Lee
Y
Jarjoura
D
Ruppert
AS
Liu
CG
Hsu
JC
Hagan
JP
.
2008
.
A comparison of normalization techniques for microRNA microarray data
.
Stat Appl Genet Mol Biol
 .
7
:
Article22
.
Raver-Shapira
N
Marciano
E
Meiri
E
Spector
Y
Rosenfeld
N
Moskovits
N
Bentwich
Z
Oren
M
.
2007
.
Transcriptional activation of miR-34a contributes to p53-mediated apoptosis
.
Mol Cell
 .
26
:
731
743
.
Rehmsmeier
M
Steffen
P
Hochsmann
M
Giegerich
R
.
2004
.
Fast and effective prediction of microRNA/target duplexes
.
RNA
 .
10
:
1507
1517
.
Reyes-Herrera
PH
Ficarra
E
.
2012
.
One decade of development and evolution of microRNA target prediction algorithms
.
Genomics Proteomics Bioinformatics
 .
10
:
254
263
.
Riley
T
Sontag
E
Chen
P
Levine
A
.
2008
.
Transcriptional control of human p53-regulated genes
.
Nat Rev Mol Cell Biol
 .
9
:
402
412
.
Risbud
RM
Porter
BE
.
2013
.
Changes in microRNA expression in the whole hippocampus and hippocampal synaptoneurosome fraction following pilocarpine induced status epilepticus
.
PLoS One
 .
8
:
e53464
.
Roach
ES
DiMario
FJ
Kandt
RS
Northrup
H
.
1999
.
Tuberous Sclerosis Consensus Conference: recommendations for diagnostic evaluation. National Tuberous Sclerosis Association
.
J Child Neurol
 .
14
:
401
407
.
Roach
ES
Sparagana
SP
.
2004
.
Diagnosis of tuberous sclerosis complex
.
J Child Neurol
 .
19
:
643
649
.
Rokavec
M
Li
H
Jiang
L
Hermeking
H
.
2014
.
The p53/miR-34 axis in development and disease
.
J Mol Cell Biol
 .
6
:
214
230
.
Romaker
D
Kumar
V
Cerqueira
DM
Cox
RM
Wessely
O
.
2014
.
MicroRNAs are critical regulators of tuberous sclerosis complex and mTORC1 activity in the size control of the Xenopus kidney
.
Proc Natl Acad Sci USA
 .
111
:
6335
6340
.
Rosahl
TW
Spillane
D
Missler
M
Herz
J
Selig
DK
Wolff
JR
Hammer
RE
Malenka
RC
Sudhof
TC
.
1995
.
Essential functions of synapsins I and II in synaptic vesicle regulation
.
Nature
 .
375
:
488
493
.
Sano
T
Reynolds
JP
Jimenez-Mateos
EM
Matsushima
S
Taki
W
Henshall
DC
.
2012
.
MicroRNA-34a upregulation during seizure-induced neuronal death
.
Cell Death Dis
 .
3
:
e287
.
Selbach
M
Schwanhausser
B
Thierfelder
N
Fang
Z
Khanin
R
Rajewsky
N
.
2008
.
Widespread changes in protein synthesis induced by microRNAs
.
Nature
 .
455
:
58
63
.
Shin
J
Pan
H
Zhong
XP
.
2012
.
Regulation of mast cell survival and function by tuberous sclerosis complex 1
.
Blood
 .
119
:
3306
3314
.
Somel
M
Guo
S
Fu
N
Yan
Z
Hu
HY
Xu
Y
Yuan
Y
Ning
Z
Hu
Y
Menzel
C
et al
2010
.
MicroRNA, mRNA, and protein expression link development and aging in human and macaque brain
.
Genome Res
 .
20
:
1207
1218
.
Somel
M
Liu
X
Tang
L
Yan
Z
Hu
H
Guo
S
Jiang
X
Zhang
X
Xu
G
Xie
G
et al
2011
.
MicroRNA-driven developmental remodeling in the brain distinguishes humans from other primates
.
PLoS Biol
 .
9
:
e1001214
.
Song
YJ
Tian
XB
Zhang
S
Zhang
YX
Li
X
Li
D
Cheng
Y
Zhang
JN
Kang
CS
Zhao
W
.
2011
.
Temporal lobe epilepsy induces differential expression of hippocampal miRNAs including let-7e and miR-23a/b
.
Brain Res
 .
1387
:
134
140
.
Tak
PP
Firestein
GS
.
2001
.
NF-kappaB: a key role in inflammatory diseases
.
J Clin Invest
 .
107
:
7
11
.
Thiele
EA
.
2004
.
Managing epilepsy in tuberous sclerosis complex
.
J Child Neurol
 .
19
:
680
686
.
Thounaojam
MC
Kaushik
DK
Basu
A
.
2013
.
MicroRNAs in the brain: it's regulatory role in neuroinflammation
.
Mol Neurobiol
 .
47
:
1034
1044
.
Vaishnavi
V
Manikandan
M
Tiwary
BK
Munirajan
AK
.
2013
.
Insights on the functional impact of microRNAs present in autism-associated copy number variants
.
PLoS One
 .
8
:
e56781
.
Vinaitheerthan
M
Wei
J
Mizuguchi
M
Greco
A
Barness
EG
.
2004
.
Tuberous sclerosis: immunohistochemistry expression of tuberin and hamartin in a 31-week gestational fetus
.
Fetal Pediatr Pathol
 .
23
:
241
249
.
Viprey
VF
Corrias
MV
Burchill
SA
.
2012
.
Identification of reference microRNAs and suitability of archived hemopoietic samples for robust microRNA expression profiling
.
Anal Biochem
 .
421
:
566
572
.
Wang
N
Zhu
M
Tsao
SW
Man
K
Zhang
Z
Feng
Y
.
2013
.
MiR-23a-mediated inhibition of topoisomerase 1 expression potentiates cell response to etoposide in human hepatocellular carcinoma
.
Mol Cancer
 .
12
:
119
.
Wang
N
Zhu
M
Wang
X
Tan
HY
Tsao
SW
Feng
Y
.
2014
.
Berberine-induced tumor suppressor p53 up-regulation gets involved in the regulatory network of MIR-23a in hepatocellular carcinoma
.
Biochim Biophys Acta
 .
1839
:
849
857
.
Wang
X
Liu
P
Zhu
H
Xu
Y
Ma
C
Dai
X
Huang
L
Liu
Y
Zhang
L
Qin
C
.
2009
.
miR-34a, a microRNA up-regulated in a double transgenic mouse model of Alzheimer's disease, inhibits bcl2 translation
.
Brain Res Bull
 .
80
:
268
273
.
Weiner
HL
Ferraris
N
LaJoie
J
Miles
D
Devinsky
O
.
2004
.
Epilepsy surgery for children with tuberous sclerosis complex
.
J Child Neurol
 .
19
:
687
689
.
You
G
Yan
W
Zhang
W
Wang
Y
Bao
Z
Li
S
Li
G
Song
Y
Kang
C
Jiang
T
.
2012
.
Significance of miR-196b in tumor-related epilepsy of patients with gliomas
.
PLoS One
 .
7
:
e46218
.
Yue
D
Liu
H
Huang
Y
.
2009
.
Survey of computational algorithms for microRNA target prediction
.
Curr Genomics
 .
10
:
478
492
.
Zhao
X
Wu
J
Zheng
M
Gao
F
Ju
G
.
2012
.
Specification and maintenance of oligodendrocyte precursor cells from neural progenitor cells: involvement of microRNA-7a
.
Mol Biol Cell
 .
23
:
2867
2878
.