Unraveling the characteristics of microRNA regulation in the developmental and aging process of the human brain
- Weiguo Li†1,
- Lina Chen†1Email author,
- Wan Li†1,
- Xiaoli Qu1,
- Weiming He2,
- Yuehan He1,
- Chenchen Feng1,
- Xu Jia1,
- Yanyan Zhou1,
- Junjie Lv1,
- Binhua Liang3,
- Binbin Chen1 and
- Jing Jiang1
© Li et al.; licensee BioMed Central Ltd. 2013
Received: 15 April 2013
Accepted: 3 December 2013
Published: 9 December 2013
Structure and function of the human brain are subjected to dramatic changes during its development and aging. Studies have demonstrated that microRNAs (miRNAs) play an important role in the regulation of brain development and have a significant impact on brain aging and neurodegeneration. However, the underling molecular mechanisms are not well understood. In general, development and aging are conventionally studied separately, which may not completely address the physiological mechanism over the entire lifespan. Thus, we study the regulatory effect between miRNAs and mRNAs in the developmental and aging process of the human brain by integrating miRNA and mRNA expression profiles throughout the lifetime.
In this study, we integrated miRNA and mRNA expression profiles in the human brain across lifespan from the network perspective. First, we chose the age-related miRNAs by polynomial regression models. Second, we constructed the bipartite miRNA-mRNA regulatory network by pair-wise correlation coefficient analysis between miRNA and mRNA expression profiles. At last, we constructed the miRNA-miRNA synergistic network from the miRNA-mRNA network, considering not only the enrichment of target genes but also GO function enrichment of co-regulated target genes.
We found that the average degree of age-related miRNAs was significantly higher than that of non age-related miRNAs in the miRNA-mRNA regulatory network. The topological features between age-related and non age-related miRNAs were significantly different, and 34 reliable age-related miRNA synergistic modules were identified using Cfinder in the miRNA-miRNA synergistic network. The synergistic regulations of module genes were verified by reviewing miRNA target databases and previous studies.
Age-related miRNAs play a more important role than non age-related mrRNAs in the developmental and aging process of the human brain. The age-related miRNAs have synergism, which tend to work together as small modules. These results may provide a new insight into the regulation of miRNAs in the developmental and aging process of the human brain.
KeywordsHuman brain Development Aging miRNA Synergistic regulation
Structure and function of the human brain change dynamically during its development and aging. The molecular and structural transformations, which form the human cognitive function, occur mainly in the period between birth and adulthood, and some developmental processes extend into adulthood, such as cortical axon myelinization [1–3]. The aging process of human brain begins at early adulthood. The aging-related changes include a decrease of brain volume, loss of synapses, and cognitive decline [2, 4–6]. In later life, the brain starts to change in a more destructive manner, which leads to a continuous cognitive decline and a rise in the frequency of neurological disorders including Alzheimer’s disease and Parkinson’s disease [7–9]. Although the changes in the developmental and aging process of the human brain are clearly observed in histology and cognitive function, the underlining molecular mechanisms are not well understood.
MicroRNAs (miRNAs) are a class of small non-coding RNAs that regulate gene expression by promoting degradation or repressing translation of target mRNAs in post-transcriptional level. Moreover, some miRNAs have also been observed to activate transcription and translation of the targets [10, 11]. Many studies have demonstrated that miRNAs play important roles in many biological functions and human diseases, such as cell proliferation, differentiation, development, apoptosis, neuronal development, differentiation, synaptic plasticity, and tumor development . In the developmental process of the human brain, several lines of evidence indicated that miRNAs contribute to the control of the development, functional and structural reorganization of the human brain . For example, neuron-specific miR-124 promotes neuronal differentiation by directly targeting PTB, which encodes a global repressor for alternative pre-mRNA splicing in non-neuronal cells . MiR-134, which is localized to the synaptodendritic compartment of hippocampal neurons, regulates synaptic plasticity by inhibiting translation of Lim-domain–containing protein kinase 1 (LIMK1) . Interestingly, accumulated evidence indicated that specific miRNAs have been shown to be involved in brain aging and other neurodegenerative pathologies [16–18]. miRNAs can regulate pathways involved in aging, and are significantly up- or down-regulated in their expression levels . There are around 1100 miRNAs in the human genome [20, 21], which potentially regulate the majority of all human genes . Therefore, these miRNAs may guide many important biological processes ranging from proliferation, differentiation to senescence and apoptosis [23–25]. It has been shown that one miRNA could regulate hundreds of target genes . Moreover, the limited miRNAs are able to regulate a large number of genes through synergism, in which multiple miRNAs work synergistically to regulate individual genes . For example, Krek et al.  found that gene Mtpn was simultaneously regulated by miR-124, let-7b and miR-375, which is the positive evidence for cooperative miRNA control in mammals. Wu et al.  showed that 28 miRNAs could substantially inhibit the expression of p21Cip1/Waf1. Therefore, the regulations between miRNAs and predicted targets could be understood more comprehensively from the network perspective. The characteristic, that one miRNA regulates a larger number of genes and one target gene is jointly regulated by multiple miRNAs, implies a complex regulatory network between miRNAs and mRNAs. Studying this complex regulatory network and the synergism of miRNAs would provide new insights into the molecular basis of miRNA functions at a system level.
Traditionally, development and aging are studied separately, which may not completely interpret the physiological mechanism over the entire lifespan. It was recently found that the majority of miRNAs and gene expression changes occurring in aging represent reversals or extensions of developmental patterns . Thus, it is necessary to study the regulatory effect between miRNAs and mRNAs in the developmental and aging process of the human brain.
Data used in the study
The mRNA and miRNA expression data (GSE18069) in the prefrontal cortex of humans was downloaded from the GEO database . It contains 23 cognitively healthy individuals with ages ranging from 2 days to 98 years old. The mRNA expression profile was measured using the Affymetrix Human Gene 1.0 ST platform and its normalized data set was downloaded. In the mRNA data, probe set identifiers (IDs) were mapped to ensemble gene IDs and mean expression level from multiple probe sets corresponding to the same gene was used to represent its expression level. The miRNA data was generated using Illumina high-throughput sequencing, which was derived from the analysis of the miRNA expression in 12 subjects selected from the individuals studied at the mRNA level. The abundance of miRNAs was normalized as RPM (reads per million reads) .
Candidate human miRNA–target relationships were acquired from miRNA target databases: TargetScan , miRanda , DIANA-microT , PicTar5 , RNAhybrid , RNA22 , PITA , MirTarget , TargetMiner  and mirSVR . In order to improve the reliability of the predicted miRNA regulations, the regulations that were stored in at least three databases were extracted for our study.
Selection of age-related miRNAs
where y ij is the expression level for gene i with i = 1, ⋯, m and sample j with j = 1, ⋯, n, A j is the age of the sample j, and e ij is the error term.
by means of an F-test. We chose the model with the highest “adjusted r2” value as the best choice.
To generate the “age-related” miRNAs, the significance of the chosen regression model was estimated with the F-test, and the FDR was calculated by 1000 random permutations of age. The median of the permutation distribution was used as the null expectation. For the miRNA data set, miRNAs with an age-test FDR < 0.1% were defined as “age-related”. Analyses were conducted in the R environment. The R code used in the analyses can be found at http://www.picb.ac.cn/Comparative/data.html.
Construction of the miRNA-mRNA regulatory network
MiRNAs can regulate mRNAs through binding to its 3′UTR, and can also regulate other miRNAs through indirect regulation. To comprehensively interpret the possible miRNA-mRNA regulatory effects at the whole genome scale, we constructed the miRNA-mRNA regulatory network by performing pair-wise spearman correlation coefficient analysis to evaluate potential correlations between 554 miRNA and 12,281 mRNA expression levels on 12 human brain samples. False discovery rate q value (qFDR), computed by the QVALUE software , was used to evaluate the statistical significance of miRNA-mRNA pairs. The miRNA-mRNA regulatory network was constructed by assembling all the significant miRNA-mRNA pairs (qFDR < 0.05), in which nodes represented miRNAs and mRNAs, and edges represented their potential regulatory correlations.
Construction of the miRNA-miRNA synergistic network
The first part is the set of miRNA pairs, which significantly co-regulated mRNAs. Here, k 1 is the number of mRNAs regulated by both miRNAs, m 1 denotes the total number of mRNAs that were regulated by all miRNAs, n 1 represents the number of mRNAs that were correlated with one miRNA, and j 1 denotes the number of mRNAs that were correlated with the other miRNA. The second part is the set of miRNA pairs whose target mRNAs were enriched in a GO biological process. Here, k 2 is the number of mRNAs included in GO terms, m 2 is the number of mRNAs significantly co-regulated by miRNA pairs, n 2 is the number of mRNAs that could not be annotated to any GO terms, and j 2 is the number of mRNAs that are not significantly co-regulated by miRNA pairs and are also annotated to the GO terms.
Topological measurements of network
For the two constructed networks, we analyzed several topological features. For the whole network, we examined the degree distribution of the network. The nodes degree distribution N(k) was defined to be the number of nodes with degree k. We also calculated degree, clustering coefficient and average shortest path length of nodes. The degree of a node is the number of edges linked to the node . The average degree of nodes was the mean degree value of all nodes in a certain set. The shortest path is a path with the smallest number of links between two nodes. The average shortest path length of a node is the average length of the shortest paths between the node and any other nodes. For a given subset of nodes, we defined its characteristic path length as the average shortest path length between any two nodes of the set.
Identification of age-related miRNA synergistic modules
We applied the Cfinder , a software based on the clique percolation clustering method, to identify miRNA synergistic modules from the miRNA-miRNA synergistic network. We defined modules as cliques, which are maximal complete subgraphs in the network. In each clique, every two miRNAs in the subgraph were connected by an edge.
For the purpose of selecting the age-related miRNA synergistic modules, we calculated the proportion of age-related miRNAs in modules and tested the correlation between the expression levels of the modules and age. The average expression of all miRNAs in a module was used to represent the overall expression level of the module. We used Pearson’s correlation coefficient to evaluate the correlation between the expression levels of the modules and age.
We evaluated the significance of the proportion of age-related miRNAs in modules and the correlation of the expression levels of the modules with age by randomly selecting miRNAs as miRNA modules. For each miRNA module, we randomly selected 1000 modules with the same number of miRNAs, calculated the proportion of age-related miRNAs in modules and evaluated the correlation between the expression levels of the modules and age. Modules with both the proportion and the correlation greater than the value in the real condition were recorded. The significance P-value was the fraction of these modules in 1000.
Using polynomial regression models, following Somel et al.  (see ‘Methods’ section), we found 98 age-related miRNAs (FDR < 0.001), whose expression levels showed significant changes with age.
MiRNA-mRNA regulatory network
The preliminary miRNA-mRNA regulatory network was first constructed by performing pair-wise spearman correlation coefficient analysis between miRNA and mRNA expression profiles. In this network, we detected 36618 significantly correlated miRNA-mRNA pairs (qFDR < 0.05). These significant miRNA-mRNA pairs were then assembled to form the final miRNA-mRNA regulatory network. The resulted network consisted of 36618 regulations between 401 miRNAs and 7175 mRNAs which represented potential regulatory correlation between miRNAs and mRNAs at the whole genome-scale. 93.5% of the miRNAs regulated at least two mRNAs and 70.4% of mRNAs were co-regulated by over two miRNAs. These results demonstrated a complicated combination in terms of target-mRNA multiplicity.
MiRNA-miRNA synergistic network
To further evaluate synergy of miRNAs in the network, we generated random miRNA-miRNA synergistic network by keeping the degree of each node unchanged using the ‘RandomNetworks’ plugin of Cytoscape. The clustering coefficients of the random network were significantly smaller than those of the actual network (p < 2.2e-16). The average clustering coefficient of the actual network was 0.5221 compared to 0.1605 of the random network, suggesting the dense local neighborhoods of the actual network. The immediate neighbors of a miRNA tend to be synergistic, which are functional synergistic partners. The dense neighborhood feature of the network could be used to predict synergism, as has been shown in previous studies .
The further investigation of the expression pattern of connected miRNA pairs by calculating their correlation coefficients supported the above hypothesis. It was found that 69% of miRNA pairs had positive co-expression values. This result indicated that most miRNA pairs with synergistic regulations tend to be co-expressed in the developmental and aging process of the human brain. We concluded that the similar expression tendency might ensure synergistic regulations among multiple miRNAs.
The topological properties of age-related miRNAs and non age-related miRNAs
Mean of AverShortPath
Mean of degree
Mean of ClusgCoeff
Age-related miRNA synergistic modules
Since age-related miRNAs had the higher clustering coefficients and lower characteristic path lengths, and were close to each other, they appeared to implemente regulations as modules. To identify these modules in the synergistic network, we applied Cfinder. All miRNAs in one module were fully connected with each other.
We found 34 target-mRNAs regulated by at least two miRNAs in clique 103. The regulations between miRNAs and mRNAs were validated by miRNA target databases. This result indicated that miRNAs in clique 103 have synergistic regulations. Furthermore, these 34 target-mRNAs were regulated by hsa-mir-29a, hsa-mir-29b, hsa-mir-29c, hsa-mir-452 and hsa-mir-1266, and were all related to neuronal development, neurodegenerative diseases and aging-related disorders.
Hsa-mir-29a, hsa-mir-29b and hsa-mir-29c are members of the miR-29 family. It is reported that hsa-mir-29a and hsa-mir-29b were down-regulated in the frontal cortex of Alzheimer’s disease (AD), which affected neurodegenerative processes . Moreover, mir-29a, mir-29b and mir-29c were significantly up-regulated, which suggested that they are most likely to play important roles in the developmental and physiological processes during brain development . Hsa-mir-452 was reported to be over-expressed in the WNT signaling associated medulloblastomas . Hsa-mir-1266’s target site spans the rs27072 SNP locus, which was significantly associated with bipolar disorder .
The gene NAV3 was synergistically regulated by hsa-mir-29a, hsa-mir-29b, hsa-mir-29c and hsa-mir-452. It was shown that NAV3 expression was enhanced in degenerating pyramidal neurones in the cerebral cortex of AD, while miR-29a was found significantly down-regulated. This observation suggested that under-expression of miR-29a affected neurodegenerative processes by enhancing neuronal NAV3 expression in AD brains . In neuroblastomas, the expression of NAV3 decreased but were up-regulated in nerve cells after brain injury, indicating that NAV3 is involved in neuron growth and regeneration as well as neural tumorigenesis . The gene ARFGEF2 was synergistically regulated by hsa-mir-29a, hsa-mir-29b and hsa-mir-29c. Mutations in ARFGEF2 implicated vesicle trafficking in neural progenitor proliferation and migration in the human cerebral cortex, which was an important regulator of proliferation and migration during human cerebral cortical development . The gene ITPKB was synergistically regulated by hsa-mir-29b and hsa-mir-452, which involved in neuronal calcium dependent signaling, a cellular process related to both AD and aging .
In this study, we integrated miRNA and mRNA expression profiles generated from the samples of the human brain across lifespan to construct the miRNA-mRNA regulatory network and the miRNA-miRNA synergistic network. By exploring these two networks, we found that there were significant differences in terms of topological features between age-related miRNAs and non age-related miRNAs. We also found that age-related miRNAs played more important roles than non age-related miRNAs in the developmental and aging process of the human brain. Moreover, the age-related miRNAs tended to work together as modules to affect multiple target mRNAs and have direct or indirect functional synergy in the developmental and aging process of the human brain and in neurodegenerative diseases. Our results were verified by reviewing miRNA target databases and the previous studies.
Most importantly, we revealed the comprehensive regulatory relationships throughout the lifespan. Studying both development and aging simultaneously could revolutionize the methodology in studying structure and function of the human brain and improved our understanding of the physiological regulatory mechanism. Furthermore, we examined the miRNA-mRNA correlations at the whole genome-scale by performing pair-wise spearman correlation coefficient analysis. The relationships based on miRNA target databases can obtain direct regulation between miRNAs and mRNAs, while merely measuring target gene expression may not be sufficient to understand the regulatory effects of miRNAs. As a result, correlation analysis could reveal associations between miRNAs and their target genes as well as non-target genes. Obviously, the synergism of miRNA was implied by the evidence that most mRNAs were co-regulated by over two miRNAs in the miRNA-mRNA network. We obtained the reliable synergism of miRNAs at a system level in the developmental and aging process of the human brain. We considered not only the co-regulation of target genes but also GO function enrichment of co-regulated targets when we constructed the miRNA-miRNA synergistic network, because miRNAs are synergistic in complex diseases and physiological processes, and regulate genes with the same or similar functions.
Our study throws a new light on miRNAs in the developmental/aging system. Also, this work can be extended to study other human tissues if the data is available.
Studying the complex regulatory network between miRNAs and their target genes and the synergism of miRNAs provided more comprehensive understanding of the molecular basis of miRNA functions at a system-wide level.
There are some limitations in our study. First, the miRNA-mRNA correlations are based on pair-wise correlation coefficient analysis. Although we examined the miRNA-mRNA correlations at the whole genome-scale, the identified regulatory correlations might contain false positives. Second, with the limited knowledge of regulation between miRNAs and the developmental and aging process of the human brain, we were unable to complete biological evidences for age-related miRNA synergistic modules. Third, the sample size of the expression profiles was too small. We hope that more comprehensive data could be obtained in the future. Despite these limitations, our study still provides a new insight into the regulation of miRNAs in the developmental and aging process of the human brain.
In conclusion, age-related miRNAs play more important roles than non age-related miRNAs in the developmental and aging process of the human brain. The age-related miRNAs have synergy effect, and tend to work together as modules.
This work was supported in part by the Science & Technology Research Project of the Heilongjiang Ministry of Education (Grant No. 12511271), the National Natural Science Foundation of China (Grant No. 61272388) and the Student Innovation Funds of Heilongjiang Province (Grant No. 2010-016HMU and 2012-011HLJ).
- Marsh R, Gerber AJ, Peterson BS: Neuroimaging studies of normal brain development and their relevance for understanding childhood neuropsychiatric disorders. J Am Acad Child Adolesc Psychiatry. 2008, 47: 1233-1251. 10.1097/CHI.0b013e318185e703.View ArticlePubMedPubMed CentralGoogle Scholar
- Sowell ER, Thompson PM, Toga AW: Mapping changes in the human cortex throughout the span of life. Neuroscientist. 2004, 10: 372-392. 10.1177/1073858404263960.View ArticlePubMedGoogle Scholar
- Thompson PM, Hayashi KM, Sowell ER, Gogtay N, Giedd JN, Rapoport JL, de Zubicaray GI, Janke AL, Rose SE, Semple J, et al: Mapping cortical change in Alzheimer’s disease, brain development, and schizophrenia. Neuroimage. 2004, 23 (Suppl 1): S2-S18.View ArticlePubMedGoogle Scholar
- Courchesne E, Chisum HJ, Townsend J, Cowles A, Covington J, Egaas B, Harwood M, Hinds S, Press GA: Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology. 2000, 216: 672-682. 10.1148/radiology.216.3.r00au37672.View ArticlePubMedGoogle Scholar
- Peters A, Sethares C, Luebke JI: Synapses are lost during aging in the primate prefrontal cortex. Neuroscience. 2008, 152: 970-981. 10.1016/j.neuroscience.2007.07.014.View ArticlePubMedGoogle Scholar
- Salthouse TA: When does age-related cognitive decline begin?. Neurobiol Aging. 2009, 30: 507-514. 10.1016/j.neurobiolaging.2008.09.023.View ArticlePubMedPubMed CentralGoogle Scholar
- Yankner BA, Lu T, Loerch P: The aging brain. Annu Rev Pathol. 2008, 3: 41-66. 10.1146/annurev.pathmechdis.2.010506.092044.View ArticlePubMedGoogle Scholar
- Obeso JA, Rodriguez-Oroz MC, Goetz CG, Marin C, Kordower JH, Rodriguez M, Hirsch EC, Farrer M, Schapira AH, Halliday G: Missing pieces in the Parkinson’s disease puzzle. Nat Med. 2010, 16: 653-661. 10.1038/nm.2165.View ArticlePubMedGoogle Scholar
- Lee ST, Kim M: Aging and neurodegeneration. Molecular mechanisms of neuronal loss in Huntington’s disease. Mech Ageing Dev. 2006, 127: 432-435. 10.1016/j.mad.2006.01.022.View ArticlePubMedGoogle Scholar
- Vasudevan S, Tong Y, Steitz JA: Switching from repression to activation: microRNAs can up-regulate translation. Science. 2007, 318: 1931-1934. 10.1126/science.1149460.View ArticlePubMedGoogle Scholar
- Place RF, Li LC, Pookot D, Noonan EJ, Dahiya R: MicroRNA-373 induces expression of genes with complementary promoter sequences. Proc Natl Acad Sci U S A. 2008, 105: 1608-1613. 10.1073/pnas.0707594105.View ArticlePubMedPubMed CentralGoogle Scholar
- Feng W, Feng Y: MicroRNAs in neural cell development and brain diseases. Sci China Life Sci. 2011, 54: 1103-1112. 10.1007/s11427-011-4249-8.View ArticlePubMedGoogle Scholar
- Persengiev S, Kondova I, Otting N, Koeppen AH, Bontrop RE: Genome-wide analysis of miRNA expression reveals a potential role for miR-144 in brain aging and spinocerebellar ataxia pathogenesis. Neurobiol Aging. 2011, 32: 2316 e2317-2327.View ArticleGoogle Scholar
- Makeyev EV, Zhang J, Carrasco MA, Maniatis T: The MicroRNA miR-124 promotes neuronal differentiation by triggering brain-specific alternative pre-mRNA splicing. Mol Cell. 2007, 27: 435-448. 10.1016/j.molcel.2007.07.015.View ArticlePubMedPubMed CentralGoogle Scholar
- Schratt GM, Tuebing F, Nigh EA, Kane CG, Sabatini ME, Kiebler M, Greenberg ME: A brain-specific microRNA regulates dendritic spine development. Nature. 2006, 439: 283-289. 10.1038/nature04367.View ArticlePubMedGoogle Scholar
- Cogswell JP, Ward J, Taylor IA, Waters M, Shi Y, Cannon B, Kelnar K, Kemppainen J, Brown D, Chen C, et al: Identification of miRNA changes in Alzheimer’s disease brain and CSF yields putative biomarkers and insights into disease pathways. J Alzheimers Dis. 2008, 14: 27-41.PubMedGoogle Scholar
- Hebert SS, De Strooper B: Alterations of the microRNA network cause neurodegenerative disease. Trends Neurosci. 2009, 32: 199-206. 10.1016/j.tins.2008.12.003.View ArticlePubMedGoogle Scholar
- Krichevsky AM, Sonntag KC, Isacson O, Kosik KS: Specific microRNAs modulate embryonic stem cell-derived neurogenesis. Stem Cells. 2006, 24: 857-864. 10.1634/stemcells.2005-0441.View ArticlePubMedGoogle Scholar
- Persengiev SP, Kondova II, Bontrop RE: The impact of MicroRNAs on brain aging and neurodegeneration. Curr Gerontol Geriatr Res. 2012, 2012: 359369.PubMedPubMed CentralGoogle Scholar
- Bentwich I, Avniel A, Karov Y, Aharonov R, Gilad S, Barad O, Barzilai A, Einat P, Einav U, Meiri E, et al: Identification of hundreds of conserved and nonconserved human microRNAs. Nat Genet. 2005, 37: 766-770. 10.1038/ng1590.View ArticlePubMedGoogle Scholar
- Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB: Prediction of mammalian microRNA targets. Cell. 2003, 115: 787-798. 10.1016/S0092-8674(03)01018-3.View ArticlePubMedGoogle Scholar
- Friedman RC, Farh KK, Burge CB, Bartel DP: Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009, 19: 92-105.View ArticlePubMedPubMed CentralGoogle Scholar
- Hermeking H: The miR-34 family in cancer and apoptosis. Cell Death Differ. 2010, 17: 193-199. 10.1038/cdd.2009.56.View ArticlePubMedGoogle Scholar
- Li Q, Gregory RI: MicroRNA regulation of stem cell fate. Cell Stem Cell. 2008, 2: 195-196. 10.1016/j.stem.2008.02.008.View ArticlePubMedGoogle Scholar
- Wang S, Olson EN: AngiomiRs–key regulators of angiogenesis. Curr Opin Genet Dev. 2009, 19: 205-211. 10.1016/j.gde.2009.04.002.View ArticlePubMedPubMed CentralGoogle Scholar
- Satoh J, Tabunoki H: Comprehensive analysis of human microRNA target networks. BioData Min. 2011, 4: 17-10.1186/1756-0381-4-17.View ArticlePubMedPubMed CentralGoogle Scholar
- Xu J, Li CX, Li YS, Lv JY, Ma Y, Shao TT, Xu LD, Wang YY, Du L, Zhang YP, et al: MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features. Nucleic Acids Res. 2011, 39: 825-836. 10.1093/nar/gkq832.View ArticlePubMedGoogle Scholar
- Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N: Combinatorial microRNA target predictions. Nat Genet. 2005, 37: 495-500. 10.1038/ng1536.View ArticlePubMedGoogle Scholar
- Wu S, Huang S, Ding J, Zhao Y, Liang L, Liu T, Zhan R, He X: Multiple microRNAs modulate p21Cip1/Waf1 expression by directly targeting its 3′ untranslated region. Oncogene. 2010, 29: 2302-2308. 10.1038/onc.2010.34.View ArticlePubMedGoogle Scholar
- Somel M, Guo S, Fu N, Yan Z, Hu HY, Xu Y, Yuan Y, Ning Z, Hu Y, Menzel C, et al: MicroRNA, mRNA, and protein expression link development and aging in human and macaque brain. Genome Res. 2010, 20: 1207-1218. 10.1101/gr.106849.110.View ArticlePubMedPubMed CentralGoogle Scholar
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008, 5: 621-628. 10.1038/nmeth.1226.View ArticlePubMedGoogle Scholar
- Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005, 120: 15-20. 10.1016/j.cell.2004.12.035.View ArticlePubMedGoogle Scholar
- Betel D, Wilson M, Gabow A, Marks DS, Sander C: The microRNA.org resource: targets and expression. Nucleic Acids Res. 2008, 36: D149-D153.View ArticlePubMedGoogle Scholar
- Maragkakis M, Reczko M, Simossis VA, Alexiou P, Papadopoulos GL, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K, et al: DIANA-microT web server: elucidating microRNA functions through target prediction. Nucleic Acids Res. 2009, 37: W273-W276. 10.1093/nar/gkp292.View ArticlePubMedPubMed CentralGoogle Scholar
- Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R: Fast and effective prediction of microRNA/target duplexes. RNA. 2004, 10: 1507-1517. 10.1261/rna.5248604.View ArticlePubMedPubMed CentralGoogle Scholar
- Miranda KC, Huynh T, Tay Y, Ang YS, Tam WL, Thomson AM, Lim B, Rigoutsos I: A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell. 2006, 126: 1203-1217. 10.1016/j.cell.2006.07.031.View ArticlePubMedGoogle Scholar
- Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E: The role of site accessibility in microRNA target recognition. Nat Genet. 2007, 39: 1278-1284. 10.1038/ng2135.View ArticlePubMedGoogle Scholar
- Wang X, El Naqa IM: Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics. 2008, 24: 325-332. 10.1093/bioinformatics/btm595.View ArticlePubMedGoogle Scholar
- Bandyopadhyay S, Mitra R: TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics. 2009, 25: 2625-2631. 10.1093/bioinformatics/btp503.View ArticlePubMedGoogle Scholar
- Betel D, Koppal A, Agius P, Sander C, Leslie C: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol. 2010, 11: R90-10.1186/gb-2010-11-8-r90.View ArticlePubMedPubMed CentralGoogle Scholar
- Faraway J: Practical Regression and ANOVA Using R. 2002, http://cranr-projectorg/doc/contrib/Faraway-PRApdf.Google Scholar
- Somel M, Franz H, Yan Z, Lorenc A, Guo S, Giger T, Kelso J, Nickel B, Dannemann M, Bahn S, et al: Transcriptional neoteny in the human brain. Proc Natl Acad Sci USA. 2009, 106: 5743-5748. 10.1073/pnas.0900544106.View ArticlePubMedPubMed CentralGoogle Scholar
- Storey JD, Tibshirani R: Statistical significance for genomewide studies. Proc Natl Acad Sci USA. 2003, 100: 9440-9445. 10.1073/pnas.1530509100.View ArticlePubMedPubMed CentralGoogle Scholar
- Barabasi AL, Oltvai ZN: Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004, 5: 101-113. 10.1038/nrg1272.View ArticlePubMedGoogle Scholar
- Palla G, Derenyi I, Farkas I, Vicsek T: Uncovering the overlapping community structure of complex networks in nature and society. Nature. 2005, 435: 814-818. 10.1038/nature03607.View ArticlePubMedGoogle Scholar
- Barabasi AL, Gulbahce N, Loscalzo J: Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011, 12: 68.View ArticleGoogle Scholar
- Goldberg DS, Roth FP: Assessing experimentally derived interactions in a small world. Proc Natl Acad Sci USA. 2003, 100: 4372-4376. 10.1073/pnas.0735871100.View ArticlePubMedPubMed CentralGoogle Scholar
- Shioya M, Obayashi S, Tabunoki H, Arima K, Saito Y, Ishida T, Satoh J: Aberrant microRNA expression in the brains of neurodegenerative diseases: miR-29a decreased in Alzheimer disease brains targets neurone navigator 3. Neuropathol Appl Neurobiol. 2010, 36: 320-330. 10.1111/j.1365-2990.2010.01076.x.View ArticlePubMedGoogle Scholar
- Podolska A, Kaczkowski B, Kamp Busk P, Sokilde R, Litman T, Fredholm M, Cirera S: MicroRNA expression profiling of the porcine developing brain. PLoS One. 2011, 6: e14494-10.1371/journal.pone.0014494.View ArticlePubMedPubMed CentralGoogle Scholar
- Gokhale A, Kunder R, Goel A, Sarin R, Moiyadi A, Shenoy A, Mamidipally C, Noronha S, Kannan S, Shirsat NV: Distinctive microRNA signature of medulloblastomas associated with the WNT signaling pathway. J Cancer Res Ther. 2010, 6: 521-529. 10.4103/0973-1482.77072.View ArticlePubMedGoogle Scholar
- Pinsonneault JK, Han DD, Burdick KE, Kataki M, Bertolino A, Malhotra AK, Gu HH, Sadee W: Dopamine transporter gene variant affecting expression in human brain is associated with bipolar disorder. Neuropsychopharmacology. 2011, 36: 1644-1655. 10.1038/npp.2011.45.View ArticlePubMedPubMed CentralGoogle Scholar
- Stringham EG, Schmidt KL: Navigating the cell: UNC-53 and the navigators, a family of cytoskeletal regulators with multiple roles in cell migration, outgrowth and trafficking. Cell Adh Migr. 2009, 3: 342-346. 10.4161/cam.3.4.9451.View ArticlePubMedPubMed CentralGoogle Scholar
- Sheen VL, Ganesh VS, Topcu M, Sebire G, Bodell A, Hill RS, Grant PE, Shugart YY, Imitola J, Khoury SJ, et al: Mutations in ARFGEF2 implicate vesicle trafficking in neural progenitor proliferation and migration in the human cerebral cortex. Nat Genet. 2004, 36: 69-76. 10.1038/ng1276.View ArticlePubMedGoogle Scholar
- Saetre P, Jazin E, Emilsson L: Age-related changes in gene expression are accelerated in Alzheimer’s disease. Synapse. 2011, 65: 971-974. 10.1002/syn.20933.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/6/55/prepub
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