Activity-Dependent Human Brain Coding/Noncoding Gene Regulatory Networks · 2013. 2. 22. ·...

INVESTIGATION

Activity-Dependent Human BrainCoding/Noncoding Gene Regulatory Networks

Leonard Lipovich,* Fabien Dachet,† Juan Cai,† Shruti Bagla,†

Karina Balan,† Hui Jia,* and Jeffrey A. Loeb*,†,1

*Center for Molecular Medicine and Genetics and †Department of Neurology, Wayne State University School of Medicine, Detroit,Michigan 48202

ABSTRACT While most gene transcription yields RNA transcripts that code for proteins, a sizable proportion of the genome generatesRNA transcripts that do not code for proteins, but may have important regulatory functions. The brain-derived neurotrophic factor(BDNF ) gene, a key regulator of neuronal activity, is overlapped by a primate-specific, antisense long noncoding RNA (lncRNA) calledBDNFOS. We demonstrate reciprocal patterns of BDNF and BDNFOS transcription in highly active regions of human neocortex removedas a treatment for intractable seizures. A genome-wide analysis of activity-dependent coding and noncoding human transcription usinga custom lncRNA microarray identified 1288 differentially expressed lncRNAs, of which 26 had expression profiles that matchedactivity-dependent coding genes and an additional 8 were adjacent to or overlapping with differentially expressed protein-codinggenes. The functions of most of these protein-coding partner genes, such as ARC, include long-term potentiation, synaptic activity, andmemory. The nuclear lncRNAs NEAT1, MALAT1, and RPPH1, composing an RNAse P-dependent lncRNA-maturation pathway, werealso upregulated. As a means to replicate human neuronal activity, repeated depolarization of SY5Y cells resulted in sustained CREBactivation and produced an inverse pattern of BDNF-BDNFOS co-expression that was not achieved with a single depolarization. RNAi-mediated knockdown of BDNFOS in human SY5Y cells increased BDNF expression, suggesting that BDNFOS directly downregulatesBDNF. Temporal expression patterns of other lncRNA-messenger RNA pairs validated the effect of chronic neuronal activity on thetranscriptome and implied various lncRNA regulatory mechanisms. lncRNAs, some of which are unique to primates, thus appear tohave potentially important regulatory roles in activity-dependent human brain plasticity.

THE availability of mammalian genome sequences hasmade it possible to delineate the boundaries and struc-tures of all genes in a genome and has demonstrated anabundance of non-protein-coding transcriptional units thatrivals the numbers of known protein-coding genes (reviewedin Carninci and Hayashizaki 2007). Complex and potentiallyfunctional regulatory relationships between protein-codingand noncoding genes, including noncoding RNA genes thatare poorly conserved across different species, have recentlybeen delineated (Katayama et al. 2005; Engstrom et al.2006). These long noncoding RNA (lncRNA) genes can bedefined by four fundamental criteria: encoding transcriptsthat lack any open reading frames (ORFs) .100 amino

acids or possessing protein database homologies (Dingeret al. 2008); being within the known range of lengths ofmammalian mRNAs; support by transcript-to-genome align-ments from complementary DNA (cDNA) data; and absenceof matches to any known noncoding-RNA classes. Function-ally, lncRNAs can have regulatory effects on coding mRNAsthrough a number of mechanisms, including those involvingendogenous antisense lncRNA transcripts that repress theirsense-strand protein-coding partners (Katayama et al. 2005;Yu et al. 2008).

Endogenous lncRNAs can also have catalytic roles, asexemplified by the TERC telomerase RNA, and by the RNAseP and MRP RNAs required for processing of other RNAs.lncRNAs essential to nuclear architecture include NEAT1and NEAT2. Nuclear hormone receptors, homeobox tran-scription factors, tumor suppressors, and immune regulatorsare all endogenously modulated by lncRNAs (reviewed inLipovich et al. 2010). Numerous lncRNAs are transcribed inthe vicinity of known protein-coding genes and regulate

Copyright © 2012 by the Genetics Society of Americadoi: 10.1534/genetics.112.145128Manuscript received February 9, 2012; accepted for publication August 24, 2012Supporting information is available online at http://www.genetics.org/lookup/suppl/doi:10.1534/genetics.112.145128/-/DC1.1Corresponding author: Department of Neurology, Wayne State University, 421E. Canfield St., 3122 Elliman, Detroit, MI 48201. E-mail: [email protected]

Genetics, Vol. 192, 1133–1148 November 2012 1133

http://www.genetics.org/lookup/suppl/doi:10.1534/genetics.112.145128/-/DC1http://www.genetics.org/lookup/suppl/doi:10.1534/genetics.112.145128/-/DC1mailto:[email protected]

those known genes through epigenetic mechanisms. Regu-lation of protein-coding genes by overlapping, or nearbyencoded, lncRNAs is central in cancer, cell cycle, and reprog-ramming (reviewed in Lipovich et al. 2010; Loewer et al.2010; Orom et al. 2010). lncRNAs encoded in an antisenseorientation to, and overlapping with, known protein-codinggenes are particularly abundant, and the small numberof antisense lncRNAs characterized to date is repletewith novel functions. Endogenous antisense lncRNAs areessential in mammalian X-inactivation (Tian et al. 2010);can directly regulate tumor suppressors; function throughdicer-independent mechanisms; and may be rapidly evolv-ing or not conserved, raising the potential for new regula-tion of old genes over evolutionary time (Lipovich et al.2010). RNA interference (RNAi) and overexpression oflncRNAs in cell lines generate reproducible phenotypes, aswe and others have shown (Bernard et al. 2010; SheikMohamed et al. 2010). Hundreds of human lncRNAs bindthe polycomb repressor complex 2 (PRC2), a key epigeneticnegative regulator (Khalil et al. 2009). In addition to high-throughput evidence of interactions with epigenetic factors,specific epigenetic roles of lncRNAs are beginning to be de-fined. Antisense lncRNAs actively and specifically modulategene expression by serving as effectors of epigenetic changesat target loci (Yu et al. 2008). These changes include anti-sense lncRNA-mediated epigenetic silencing of the sense-strand protein-coding gene promoter; such silencing can beabrogated by Argonaute-2-dependent, small-RNA-mediatedsuppression of the antisense lncRNA, resulting in “RNA ac-tivation” of the sense gene (Morris et al. 2008). Promoter-overlapping antisense lncRNAs can also be targeted by ex-ogenous short RNAs that regulate sense gene expression,also via Argonaute (Schwartz et al. 2008). Despite thesepromising examples, a majority of the thousands of otherlncRNAs evident in transcriptome data still remain devoid ofassigned functions.

This abundance of lncRNAs, many of which are primate-specific, warrants a systematic assessment of whether theyhave functional, regulatory roles. Perhaps nowhere mightthis be more important than in the human brain that iscomposed of a diverse set of cell types connected throughcomplex synaptic arrangements. The degree of synapticactivity in the brain can be translated into functional andstructural changes through activity-dependent changes ingene expression (Katz and Shatz 1996). Although thesechanges can be effected through direct activation of synapticgenes, they can also be achieved through the release ofneurotrophic factors such as brain-derived neurotrophic fac-tor (BDNF) that have direct effects on synaptic architectureand indirect effects by producing changes in gene expression(Isackson et al. 1991; Binder et al. 2001). BDNF, a memberof the nerve growth factor family, regulates the survival anddifferentiation of neuronal populations, axonal growth andpathfinding, and dendritic growth and morphology and hasbeen linked to many human brain disorders (reviewed inBibel and Barde 2000; Binder and Scharfman 2004; Hu

and Russek 2008). BDNF messenger (mRNA) and proteinare upregulated by seizure activity in animal models of ep-ilepsy as well as in human brain tissues that display in-creased epileptic activities (Ernfors et al. 1991; Lindvallet al. 1994; Nibuya et al. 1995; Beaumont et al. 2012).The genomic locus encoding BDNF is structurally complexand also encodes BDNFOS, a primate-specific lncRNA thatis antisense to the coding BDNF gene (Liu et al. 2006; Aidet al. 2007; Pruunsild et al. 2007). BDNF and BDNFOSform double-stranded duplexes, suggesting a potential forBDNFOS to post-transcriptionally regulate BDNF (Pruunsildet al. 2007). Antisense knockdown of BDNFOS, in fact, hasrecently been shown to increase BDNF expression in HEK293cells and promotes neuronal outgrowth in vitro (Modarresiet al. 2012)

BDNF binding to its receptors results in a diverse array ofdownstream signaling pathways including the activation ofcyclic adenosine monophosphate response element bindingprotein (CREB), which, in turn, can also regulate BDNF bybinding to a cognate site within the BDNF gene (Tao et al.1998; Spencer et al. 2008). Activation of CREB by phosphor-ylation at serine 106 as a result of neuronal activity leads tochanges in gene expression that cause reinforcement andstabilization of more active neuronal circuits (reviewed inHerdegen and Leah 1998; Kandel 2001; Matynia et al. 2002;West et al. 2002). Downstream from phosphorylated CREB(pCREB), immediate early genes have been shown to medi-ate long-lasting changes in neuronal structure and excitabil-ity. Upstream of CREB activation, several known signalingpathways are rapidly activated in response to neuronal ac-tivity (Kandel 2001; reviewed in West et al. 2002), includingCaMKinase IV, protein kinase A, and MAPK. We have re-cently observed a pattern of transcriptional activation inhuman brain regions where seizures start that stronglyimplicates sustained MAPK/CREB activation and down-stream coding gene activations that could underlie layer-specific changes in synaptic architecture that makes theseregions prone to seizures (Rakhade et al. 2005; Barkmeieret al. 2012; Beaumont et al. 2012).

Given that human lncRNA genes tend to be less well-conserved than protein-coding genes, and can give rise tounique transcripts not found in other species, we sought outa uniquely human system to examine activity-dependentgene expression for both coding and noncoding RNAs usinga pairwise comparison of human cortical regions displayingvariable degrees of epileptic activities. These brain regionswere removed as part of surgical treatment for intractableseizures. We show that regions of human neocortex thatdisplay increased activity and BDNF expression have re-duced BDNFOS expression and that BDNFOS directly down-regulates BDNF in vitro in a neuronal cell line. We developeda custommicroarray platform to perform a transcriptome-widediscovery of other regulatory lncRNAs and matched these tonearby or overlapping, differentially expressed protein-codinggenes to develop a genome-wide list of lncRNA–mRNA genepairs. Many of the coding mRNAs identified in this way are

1134 L. Lipovich et al.

known to modulate activity-dependent gene expression inthe human brain, suggesting that these lncRNA–mRNA pairsform a newly revealed regulatory network of human brainplasticity.

Materials and Methods

Human brain tissue

Informed consent was obtained from seven patients whounderwent surgery for medically intractable epilepsy. Ex-treme care was taken to ensure that our study did notinfluence surgical decision making. All patients underwentpresurgical evaluation and identification of epileptic andcontrol regions as previously described (Rakhade et al. 2005;Beaumont et al. 2012). To localize epileptic brain regions thatdisplayed both clinical seizures and interictal epileptiformdischarges (spikes), a two-stage surgical approach usingsubdural electrodes with continuous brain-surface record-ings (electrocorticography) and video monitoring was un-dertaken for 2–5 days. For these studies, paired tissuesamples from neocortex within each patient displaying highand low amounts of interictal (between seizures) spikingwere used to compare differential gene expression as a func-tion of brain activity (Loeb 2010). To avoid introducingadditional variables into the analysis, each block of tissuewas examined histopathologically and demonstrated a nor-mal six-layered neocortical structure without lesions. Thepaired analysis of high- and low-spiking neocortex withineach patient is also critical to isolate the variable understudy, which is the degree of activity. Total RNA was pre-pared using a modification of the protocol described previ-ously (Beaumont et al. 2012). The difference was that onlygray matter was used by pooling two to three nearby stripsof gray matter that extended from the pial surface to thewhite matter from each block of tissue corresponding toa given electrode location. This pooling method helps cor-rect for differences in dissections that could lead to over- orunder-representation of specific cortical layers.

Cell cultures, transfections, and depolarizations

The SH-SY5Y cell line (ATCC) was maintained in Dulbecco’smodified Eagle’s medium supplemented with 10% FBS andused for experiments. Cells between 17 and 25 passageswere transfected with BDNFOS-targeting and BC013641-targeting small interfering RNAs (siRNAs) by electropora-tion according to the manufacturer’s instructions at �80%confluence (Neon electroporation system, Invitrogen). Theelectroporation conditions used for SH-SY5Y cell transfec-tion were the following: 1200 V; pulse width: 20 ms; andnumber of pulses: 2. Prior to the experiments, these condi-tions had been optimized using a condition matrix, a controlsiRNA, and fluorescent reporters (data not shown). Singleand multiple depolarizations of cells were induced by add-ing 100 mM KCl (final concentration) to the medium atdifferent time points as indicated in the Figure 5 legend.

Quantitative PCR, siRNAs, and primers

Total RNA from cultured SH-SY5Y cells was isolated with anRNAeasy mini kit according to the manufacturer’s instruc-tions (QIAgen). The first-strand cDNA was prepared usingSuperScript First-Strand cDNA kit (Invitrogen), and mRNAand lncRNA expression levels were determined by Taqmanquantitative real-time PCR (Taqman qPCR). BDNFOS siRNAsdesignated S1, S2, S3, and S4 were custom-designed andsynthesized by Invitrogen. The BDNFOS Taqman primer/probe combos were custom-designed by uploading FASTA-format sequences of preferred amplicon regions to the ABITaqman custom-design website and were purchased fromABI/Life Technologies. This vendor does not release the ac-tual primer and probe sequences of custom-designed ampli-cons to the users. An siRNA against the housekeeping geneglyceraldehyde 3-phosphate dehydrogenase (GAPDH) wasused to rule out nonspecific effects. While this siRNAknocked down GAPDH, it had no effects on BDNF, BDNFOS,and Lin7C at 24 and 48 hr (see supporting information,Figure S1 and File S4).

Western blot analysis

Cell lysates were prepared with SDS sample buffer (Sigma)and subjected to Western blotting to measure CREB phos-phorylation as described (Beaumont et al. 2012). Briefly,proteins separated on 4–20% gradient sodium dodecyl sulfate-polyacrylamide gel were electrically transferred onto nitro-cellulose membrane. After blocking with 5% (v/v) skim milkin TBS containing 0.05% Tween-20 for 1 hr at room temper-ature (RT), the membrane was incubated with rabbit poly-clonal antibody against pCREB (Cell Signaling) at a dilutionof 1:1000 for 1 hr at RT and then with specific secondaryantibody coupled with HRP (1:5000) for 1 hr at RT. pCREBwas visualized with ECL detection system (Pierce). The mem-brane was then stripped and reprobed with CREB antibody(Cell Signaling) at (1:1000) to measure total CREB.

Custom microarrays

Seven 60-mer probes per gene, unambiguously mapping byBLAT (Kent 2002) to a single genomic location and free ofinterspersed and simple repeats, were designed using theAgilent Technologies OpenGenomics eArray interface for5586 of the 6736 lncRNA genes from Jia et al. (2010).The remaining lncRNA genes had been excluded becauseof eArray failure to yield seven probes per gene or becausethe eArray-designed probes failed our subsequent check forgenomic uniqueness and absence of repeats. As a positivecontrol, we also included seven probes each for 111 of the137 previously determined protein-coding epileptic genes(Beaumont et al. 2012) and for six housekeeping controlgenes. The eArray Fill Array feature was used to randomlyselect control protein-coding gene probes to fill all featuresthat would have otherwise remained vacant (,2% of totalfeatures on a 44,000-feature, i.e., “44k,” array cell). Theentire probe set was printed in quadruplicate on each slideusing the Agilent 4 · 44,000 high-density oligonucleotide

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microarray platform. Our custom lncRNA microarray con-tained the combined resulting set of probes (File S2).

Our Agilent 60mer probes are longer than the 25mersused on the Affymetrix platform, and more importantly, wetested each probe (after the probe was proposed by theAgilent EArray design software) for genomic mappinguniqueness [by University of California at Santa Cruz (UCSC)BLAT] and for repetitive element overlaps (by RepeatMasker).Only repeat-free and uniquely-mapping 60mer probes wereincluded. Specificity is assured by these sequence qualities ofthe probes as well as by our strategy of profiling each lncRNAgene with seven unique probe sequences (not seven replicatesof the same probe).

A dye-flip quadruplicate two-color microarray experimentwas performed on each within-patient pair of high-spikingand low-spiking surgically resected samples on both theAgilent human genome-wide array (G4112A) and ourcustom lncRNA array as described, but using a differentlabeling method (Beaumont et al. 2012). We used the Epi-centre protocol to generate aminoallyl-amplified RNA (aRNA)for subsequent amplification and labeling with either cyanineor Alexa dyes. For our custom lncRNA arrays, we used label-ing with Alexa dyes (Alexa-647 and Alexa-555, Invitrogen)within the flip-dye design, as described by the manufacturer(SuperScript Indirect RNA Amplification System, Invitro-gen) (Holloway et al. 2008). For every patient, each of thequadruplicates was hybridized on four separate slides. Fourslides of 4 · 44,000 Agilent arrays (4 arrays, each composedof the same set of �44,000 probes) were used to screenseven patients. All slides were scanned as described previ-ously (Beaumont et al. 2012).

Because our lncRNA custom microarray platform is new,we also used qPCR to validate a representative subset ofdifferentially expressed lncRNAs. We considered which spe-cific probes were responsible for the differential expression ofeach coding and noncoding gene observed across all sevenpatients and used probes to target only the region of eachtranscript that was overlapped by the differentially expressedprobes. Positive correlation coefficients were seen in all cases,ranging from 0.61 to 0.96 (File S1) between the array andqPCR results within each patient; all protein-coding gene dif-ferential expression results were from the G4112A or F cata-log protein-coding microarray, and all lncRNA results werefrom our lncRNA custom microarray (Figure S4 in File S4).

All lncRNA–mRNA overlaps in this work are in the anti-sense orientation. For all lncRNA–mRNA neighbor pairs,there is a spacer between the two genes along the genome,regardless of strand. All probes on our microarray arestrand-specific and, therefore, even in the case of anlncRNA–mRNA antisense pair, will exclusively profile eitheronly the lncRNA (on the custom array described in thisarticle) or only the mRNA (on the Agilent catalog array).

Microarray statistical methods

To identify those differentially expressed lncRNAs thatmay be directly regulating their overlapping or neighboring

protein-coding genes, we integrated our custom lncRNAexpression microarray data with our conventional mRNAexpression microarray data for the in vivo high-/low-activitycortical sample pairs from all seven patients analyzed withboth array types. For each epilepsy patient, we had a within-patient sample pair of a high-spiking and a low-spiking re-gion. This within-patient sample pair was analyzed, usingthe same dye-flip quadruplicate strategy, for both the catalogcoding (G4112A) and the custom lncRNA microarray. Dif-ferentially expressed genes were identified from both micro-array platforms but using the same strategy. Consistencybetween arrays was first examined by correlating the foldchange of all protein-coding control genes common to botharrays, which was possible because our 111 “epileptic tran-scriptome” genes from the prior protein-coding array work(Beaumont et al. 2012) were used as controls on the lncRNAarray. We used the average value of the seven probes corre-sponding to each control gene on the lncRNA custom array.For 140 catalog (Agilent G4112A) coding-array probes cor-responding to these 111 genes, Pearson’s correlation coeffi-cient was 0.90, attesting to very high reproducibility betweenthe coding array and the noncoding custom array.

Scanned microarray images from coding and noncodingmicroarrays were analyzed by the software Agilent FeatureExtraction (Agilent, V10.3.1) with the default protocolGE2_107_Sep09. A fluorescent correction factor was de-termined using both qRT-PCR and Agilent Spike-IN probes.This correction factor was then applied on the fluorescenceintensity (fluorescence at exponent 1.125) and improvedthe fold change prediction. The fluorescence distributioninside each repetition of the microarray experiments wasnormalized by R V2.11 (R Development Core Team 2010)using the library “limma” (Smyth and Speed 2003) in a two-step process: (i) normalization of the intensity of fluores-cence between dyes using a Loess correction (iterations:50; span: 0.05) and (ii) independent scaling of fluorescenceintensity on the same range across all the arrays for each dyeusing quantile normalization. The quality of the normaliza-tion process of the microarray fluorescence was validatedusing MA plot density and distribution analysis. Normalitywas asserted using the Anderson–Darling test from the li-brary Nortest (Gross 2006). For each array, the backgroundlevel was globally computed using the median of the fluo-rescence intensity of the negative control probes and sub-tracted from the signal of each probe.

Once normalized, the microarrays were further analyzedusing standard statistical methods (Kerr and Churchill2001b; Wolfinger et al. 2001). The differentially expressedgenes between high and low spiking were determined usinga two-step mixed model analysis of variance (Jin et al. 2001)with the library LME4 (Bates et al. 2009). This mixed modelapproach has been used to compute the fitted effect and therandom effects simultaneously (Littell et al. 1996). To im-prove the sensibility of the analysis (Kerr et al. 2000; Jinet al. 2001), computation did not use the ratio but insteadused dye fluorescence intensity indexed by the type of RNA


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(Tanaka et al. 2000) (RNA from the high-spiking area orRNA from the low-spiking area). The false discovery rate(FDR) and corrected P-value for each gene was computedwith “R” using the library “fdrtool” (Strimmer 2009). Thedifferentially expressed genes were detected using fold changeand significance simultaneously (Tanaka et al. 2000) and weredetermined as significantly differentially expressed if theirfold change, for at least one probe per gene, was$1.4 and iftheir FDR was #5%.

In addition to a number of custom approaches to identify8 cis-acting coding lncRNA pairs, 26 trans-acting lncRNAswere identified as significant and activity-dependent by theirtight correlation (Pearson’s correlation coefficient minimum of0.9) to a well-known group of 13 activity-dependent protein-coding genes (Rakhade et al. 2007; Beaumont et al. 2012),which themselves had been co-expressed with a Pearson’scorrelation coefficient of 0.95. These results were displayedgraphically using Cytoscape (Smoot et al. 2011). To includea trans-acting lncRNA in this group, at least one probe (ofthe seven available probes) representing the lncRNA genehad to meet this statistical requirement.

To study the genes represented both on the catalog arrayand on our custom array, we used genomic localization of alltranscripts along the same human genome assembly, hg19,to find differentially expressed transcripts from the customnoncoding and the G4112A catalog coding array that wereclose to each other along the genome or overlapped in anantisense orientation within a genomic region. The genomicposition, strand, and exon/intron location information foreach transcript is contained in the all_mrna BED file of theUCSC Genome Database.

Results

Reciprocal patterns of BDNF and BDNFOS expression inelectrically active human brain

Patients who fail to respond to medical management of theirseizures can greatly benefit from a two-stage surgical pro-cedure where long-term in vivo brain-surface recordings areused to identify and remove epileptic brain regions. We haveused this human system recently to identify a relativelysmall group of genes, including BDNF that are differentiallyexpressed in regions of the human neocortex where seizuresstart (Rakhade et al. 2005; Beaumont et al. 2012). Whileremoving seizure-onset regions is key to a good outcome forimproved seizure control, seizures from these brain regionsare relatively infrequent compared to the small, but ex-tremely frequent “interictal,” epileptic discharges that canoccur almost constantly between seizures in some brainregions (Staley et al. 2005). In fact, several of the genesinduced at seizure-onset zones correlate precisely with inter-ictal spiking rather than with seizure frequency (Rakhadeet al. 2007), suggesting that interictal spiking may be thedriving force behind this altered expression pattern. Consis-tently, an animal model of interictal spiking without seizureswas sufficient to produce neuronal layer-specific changes in

these genes (Barkmeier et al. 2012). Here we have focusedon brain regions with different levels of interictal spiking toidentify the relationships between coding and noncodingtranscripts in the in vivo human brain.

Figure 1A shows a table of seven patients used for thepresent study, together with quantified in vivo spike frequen-cies, tissue locations, and pathological descriptions. Patientsvaried in both sex and age, but were chosen because of theavailability of both high- and low-interictalspiking neocorti-cal brain samples from nearby brain regions for each patientthat were removed as part of their seizure surgery treat-ment. Figure 1B shows how each of these pairs was selectedwith a short sample of the electroencephalogram recordingthat illustrates the large difference in interictal spiking. It isimportant to emphasize that, because of genetic differences,medication effects, and effects of tissue processing, our in-ternally controlled experimental design is crucial (Rakhadeet al. 2005; Beaumont et al. 2012). Although patients arelisted with different pathological diagnoses from multipleneocortical regions, only tissue samples that showed a nor-mal cortical architecture were used so as not to influence themajor variable of interest: increased brain activity.

Because of the potential regulatory relationship of tran-scripts that code for BDNF with those that encode thepartially antisense BDNFOS, as a first step we compared therelative expression levels of BDNF and BDNFOS betweenpaired high- and low-spiking regions of human neocortexusing qPCR for each patient (Figure 1C). In most patients,BDNF expression was higher in more electrically activeregions, whereas BDNFOS lncRNA levels were significantlyreduced in the high-spiking regions. We used EGR1 expres-sion as a positive control for high-spiking human corticalbrain regions as its expression has been shown to be directlyproportionate to interictal brain activity (Rakhade et al.2007). These results raise the possibility that increasedBDNF levels could in part be regulated by a decrease ofthe antisense BDNFOS RNA.

BDNFOS is a negative regulator of BDNF in an in vitrohuman cell culture system

The genomic antisense orientation of BDNF and BDNFOSis shown in Figure 2A, where both overlapping and non-overlapping regions are delineated. We have previouslydemonstrated that perturbation of lncRNA levels at multiplecis-antisense lncRNA–mRNA pairs affects levels of the cog-nate mRNAs (Katayama et al. 2005). To distinguish whetherthe lncRNA BDNFOS directly regulates BDNF mRNA levels,we custom-designed three siRNAs targeting human BDNFOS(Figure 2A) and used qPCR to interrogate BDNFOS lncRNAand BDNF mRNA levels after the siRNA transfections. BDNFOSsiRNAs were individually transfected into the human neuro-blastoma cell line SH-SY5Y by electroporation and caused re-producible BDNFOS knockdown at 24 hr (all three siRNAs) andat 48 hr (only S2). Two of the siRNAs led to knockdown ofBDNFOS by .70% (Figure 2B). BDNFOS knockdown by thesedouble-stranded RNAs (dsRNAs) consistently led to an increase


in BDNF mRNA levels (between 1.5- and 3.5-fold-change),suggesting that the cis-antisense BDNFOS RNA functions asa negative regulator of human BDNF (Figure 2B).

lncRNA genes in gene chains—loci where three or moregenes are joined through shared antisense overlaps and bidirec-tional promoters—are a general property of the mammaliangenome (Engstrom et al. 2006). Human BDNFOS is part ofa three-gene genomic positional chain: it shares a putative bi-directional promoter with the LIN7C gene at its 59 end whilealso encompassing an exonic cis-antisense overlap with BDNFexonic sequences at its 39 end. BDNFOS knockdown by the samedsRNAs also increased the mRNA levels of LIN7C, suggestingthat BDNFOS may negatively regulate other genes at its locus.

Transcriptome-wide profiling of all known humanprotein-coding and lncRNAs reveals activity-dependentregulatory pairs and networks

Our functional validation of the primate-specific BDNF/BDNOS pair suggests the potential for many more coding/noncoding regulatory relationships across the human ge-nome that may vary as a function of brain activity. Here weutilized these same paired RNA samples from the sameseven patients to identify the activity-dependent coding/noncoding interactome. To achieve this, we developed

a custom lncRNA microarray, which allowed us to comparetranscriptional profiles of lncRNAs to coding RNAs froma commercial genome-wide coding array (Figure 3). Thisnew custom array is based on our previously defined andcharacterized human lncRNA gene catalog from experimen-tal transcriptome data represented by cDNA and EST se-quences in GenBank, totaling 6736 lncRNA transcriptionalunits (Jia et al. 2010). Our human lncRNA gene catalog ismostly nonredundant with respect to other recently pub-lished human lncRNA collections (Figure S2 in File S4). Incontrast to our custom lncRNA array, current commercialmicroarray platforms do not adequately represent manygenomically complex loci, including those encoding lncRNAgenes and sense–antisense pairs (Orlov et al. 2007; Jia et al.2010).

Both platforms utilized a dye-flip (Kerr and Churchill2001a) quadruplicate experimental design to obtain themost accurate statistical comparison of each pair of tissuesamples from each patient (Yao et al. 2004; Rakhade et al.2005; Beaumont et al. 2012). Each within-patient samplepair was analyzed, using the same dye-flip quadruplicatestrategy, for both the catalog coding (G4112A) and the cus-tom lncRNA microarray. Differentially expressed genes wereidentified from both microarray platforms, but using the

Figure 1 Reciprocal pattern of BDNFand BDNFOS gene expression in electri-cally active human neocortex. (A) Summaryof human epilepsy patients showing theratios of electrical discharges, regions ofneocortex sampled for each, and histo-pathology. All tissue sampled for geneexpression changes had a normal histo-logical structure, even in the presence ofnearby structural abnormalities. (B) Long-term brain-surface recordings obtainedprior to tissue resection were used to dif-ferentiate electrode locations with high-and low-spiking interictal activities foreach patient. (C) While both the activity-dependent immediate early gene EGR1and BDNF are constitutively upregulatedin high-spiking cortex, BDNFOS was con-sistently downregulated in the same sam-ples. The downregulation was significant(P = 0.016, Wilcoxon’s test; BDNFOS fold-change , 21.1, 95% C.I.). Bars repre-sent average values for all seven patientsshown with the color of the circles corre-sponding to the patients shown in A.


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same strategy. Consistency between arrays was first exam-ined by correlating the fold-change of all differentiallyexpressed protein-coding control genes common to botharrays, which was possible because our 111 epileptic tran-scriptome genes from the prior protein-coding array workwere used as controls on the lncRNA array. We used theaverage value of the seven probes corresponding to eachcontrol gene on the lncRNA custom array. For 140 catalog(Agilent G4112A) coding-array probes corresponding to these111 genes, Pearson’s correlation coefficient was 0.90, attest-ing to very high reproducibility between the coding array andthe noncoding custom array.

To define a gene as differentially expressed, we requiredat least one microarray oligonucleotide probe correspondingto that gene to be $1.4-fold differentially expressed withFDR #5% in a groupwise analysis of all seven patients.These thresholds were selected based on a power analysisusing this flip-dye quadruplicate design (Loeb and Beaumont2009). Using this criterion, we identified 4004 protein-codinggenes from the catalog array (1944 upregulated and 2060downregulated in high-activity areas; File S1). On thelncRNA arrays, 86 of the 111 positive control genes wereupregulated, and 1288 lncRNA genes were differentiallyexpressed between high-activity and low-activity neocortical

regions (698 upregulated lncRNA genes and 590 downregu-lated lncRNA genes in high-activity areas; File S2). BDNFwas represented on both the coding microarray and, asa brain-expressed known control gene, on the lncRNA mi-croarray. BDNF was upregulated in high-activity tissue fromall seven patients according to both our array platforms:coding microarray, median 3.6-fold change; lncRNA micro-array, median 2.8-fold change.

To integrate the coding and noncoding transcriptomes ofthe human neocortex (File S3), we then determined whichof the differentially expressed protein-coding genes wereencoded by genomic loci overlapping, or adjacent to, theloci which also encoded differentially expressed lncRNAgenes as outlined in Figure 3A. Here, we analyzed the entireextent of differential expression for lncRNAs that participatein sense–antisense pairs and in non-overlapping gene neigh-bor pairs such that one gene in the pair was protein-codingwhereas the other gene encoded the lncRNA. Specifically, weidentified all cis-encoded gene pairs in which both a protein-coding gene and a noncoding (lncRNA) gene were expressedfrom the same locus. We refer to these pairs as coding–noncodinggene pairs. We then separated the pairs into two categories—antisense and neighbor—both of which carry the potentialfor mRNA regulation by a paired lncRNA (Jia et al. 2010;Lipovich et al. 2010). We defined a cis-antisense gene pair astwo genes transcribed from the opposite strands of the samelocus in a configuration such that at least some sequence inat least one exon overlaps one exon of the other gene. Wedefined a neighbor-gene pair as any gene pair such thatthe nearest boundaries of two nearby, but nonoverlap-ping, genes are ,10 kb away from one another. In thisstudy, “cis” therefore refers to any same-locus (not neces-sarily same-allele) regulatory mechanisms, which includeantisense-mediated regulation by lncRNAs of protein-codinggenes that are encoded in the same locus. “Trans” refers toany regulation involving genes encoded at multiple distinctgenomic loci.

Of our lncRNAs differentially expressed at high-activityregions, 290 were members of sense–antisense gene pairs(File S3). We define codifferential expression as a differen-tial expression profile of two genes such that the differentialexpression of one gene is either inversely or directly corre-lated with the differential expression of the other geneacross multiple sample pairs, each of which originates froma different patient and all of which are statistically signifi-cant. Only 4 of these 290 mRNA–lncRNA cis-antisense pairswere codifferentially expressed in all seven patients (Figure3B). Only 1 of the 4 pairs (BDNFOS/BDNF) featured aninverse differential expression profile. The other 3 pairs allhad a positive, direct correlation. This is, in fact, not surpris-ing, given the prevalence of synergistic, as opposed to in-verse, expression patterns in mammalian cis-antisense pairsin response to a stimulus or to a knockdown (Katayama et al.2005). These 3 pairs featured lncRNAs cis-antisense to MAP-K1IP1L (MAP Kinase 1 Interacting Protein 1-like, potentiallya modulator of MAP Kinase 1, whose role centers on the

Figure 2 Downregulation of BDNFOS induces BDNF and LIN7C expres-sion in SH-SY5Y cells. (A) The BDNFOS/BDNF gene locus shows antisenseoverlap between BDNF and BDNFOS (UCSC Genome Browser) (Kent2002). Three siRNAs were generated from a non-overlapping BDNFOSexon (S1, S2, S3). (B) Downregulation of BDNFOS lncRNA using each ofthese siRNAs produced a corresponding increase in BDNF and, to a lesserextent, in LIN7C mRNA levels at 24 and 48 hr. Expression level changesare relative to a mock-electroporation negative control. Standard errorbars are displayed. No further BDNFOS knockdown or BDNF rescue per-sisted at the 48-hr time point for s1 and s3 (data not shown).


http://www.genetics.org/lookup/suppl/doi:10.1534/genetics.112.145128/-/DC1/genetics.112.145128-1.xlshttp://www.genetics.org/lookup/suppl/doi:10.1534/genetics.112.145128/-/DC1/genetics.112.145128-3.xlshttp://www.genetics.org/lookup/suppl/doi:10.1534/genetics.112.145128/-/DC1/genetics.112.145128-2.xlshttp://www.genetics.org/lookup/suppl/doi:10.1534/genetics.112.145128/-/DC1/genetics.112.145128-2.xls

CREB activation pathway upstream of brain activity-dependentgene expression); PURB (purine-rich element-binding pro-tein, a gene expression regulator); and C11ORF96, whichwe have shown by bioinformatic analysis to be a humanhomolog of the rat AG2 gene (Matsuo et al. 2000), inducedas a consequence of sustained long-term potentiation in vivoin rat hippocampus and therefore implicated in neuronalplasticity. In summary, the protein-coding genes at 3 of the4 codifferentially expressed lncRNA–mRNA cis-antisensepairs have known neuronal functions centered on synapticplasticity. The remaining 286 lncRNA–mRNA cis-antisensepairs did not have any correlation between the mRNA and

the lncRNA, within each antisense gene pair, across theseven patients.

A total of 276 lncRNAs differentially expressed at high-activity brain regions resided at genomic loci where a protein-coding gene and an lncRNA gene were non-overlapping butwithin 10 kb of each other along the genome (Jia et al.2010). However, only four mRNA–lncRNA neighbor-genepairs were codifferentially expressed in the groupwise anal-ysis of the seven patients (Figure 3B). These four codiffer-entially expressed neighbor-gene pairs contained lncRNAgenes neighboring the protein-coding genes ARC (activity-regulated cytoskeleton-associated), a key regulator of neuronal

Figure 3 Genome-wide analysis of human cortex reveals activity-dependent coding–noncoding gene pairs and stand-alone lncRNAs. (A) This experi-mental design of paired high- and low-spiking brain samples from the seven patients shown in Figure 1A was used to interrogate both coding andnoncoding gene transcription as a function of brain activity. A flip-dye, quadruplicate microarray design was used with both a genome-wide coding arrayand a novel custom lncRNA array encompassing 5586 lncRNA genes with seven probes per gene. Based on a rigorous statistical cutoff, a total of 4044protein-coding and 1288 lncRNA genes were identified for these seven patients (.1.4-fold; FDR ,5% for each probe). lncRNA genes were furthersubdivided based on known cis-antisense partners of protein-coding genes, lncRNAs located,10 kb from any known gene, or stand-alone lncRNAs.10kb from any known gene. Due to gene chains (Engström et al. 2006), some lncRNAs belonged simultaneously to the first two of these three categories. (B)Pairs of differentially expressed protein-coding and lncRNA genes that were either in an antisense overlap or ,10-kb neighbors are shown together withstand-alone lncRNAs. Many of the affected protein-coding genes have known functions in synaptic plasticity. The green arrows to the left of the genepairs have been validated by qPCR. *BDNFOS-BDNF was discovered by targeted qPCR and did not meet statistical significance on the microarrays.


receptor endocytosis required for both synaptic plasticityand long-term memory; L-plastin (LCP1), relevant to theactivity-dependent MAPK/CREB activation by its placementwithin the human MAPK interactome (Bandyopadhyay et al.2010); SMEK2, a regulatory subunit of Ser/Thr phosphatase4; and CYR61, a secreted protein that associates with theextracellular matrix and the cell surface, regulates Akt acti-vation (Goodwin et al. 2010), and is differentially expressedin autism (Garbett et al. 2008).

Of the 1288 lncRNA genes determined by our custommicroarray to be differentially expressed at high-activityareas of the human neocortex, 846 remain largely refractory tofunctional interpretation as they were not genomically near,or antisense to, any known protein-coding genes. These includethe lncRNA MALAT-1, originally discovered as a predictor ofmetastasis and survival in lung cancer (Ji et al. 2003) and nowknown to be a regulator of several synaptic genes (Bernard et al.2010; Lipovich et al. 2010). Some are key components of specificnuclear bodies, while other lncRNAs regulate imprinting genesand still others perform essential catalytic roles (Bernard et al.2010; reviewed in Lipovich et al. 2010). Differential expressionof 5 of these known nuclear RNAs (Figure 3B, bottom) wassignificant. MIAT, the sole member of this group that was down-regulated in the more active areas delineates a novel neuronalnuclear domain (Sone et al. 2007) shown to be both a directtarget and a putative co-activator of the transcription factor Oct4(Sheik Mohamed et al. 2010). In contrast, the levels of the other4 known nuclear lncRNAs were increased in the more activeareas. These lncRNAs included: KCNQ1OT1, which may regu-late imprinting by recruiting the DNAmethyltransferase DNMT1to differentially methylated regions (Mohammad et al. 2010);RPPH1, the catalytic-RNA component of RNase P, essential for

tRNA 59 end maturation and for regulating Pol III-dependenttRNA transcription (Jarrous and Reiner 2007); NEAT1, an es-sential component of nuclear paraspeckles that suppresses thenucleocytoplasmic export of Alu-containing RNAs; and NEAT2(MALAT-1), an essential component of nuclear speckles anda regulator of synaptic genes (Bernard et al. 2010).

We also used a second unbiased approach to identifyactivity-dependent lncRNAs with potential importance insynaptic plasticity transcriptional regulatory networks. Wehave shown that a number of coding genes, including EGR1,EGR2, and FOS, are expressed in human brain in directrelation to the degree of epileptic discharges (Rakhade et al.2007). Using co-expression clustering of protein-codinggenes, we identified these and 8 additional genes (13 total)that have the same pattern of expression across the sevenpatients and then identified 26 lncRNAs whose patternof expression correlated with this group of coding genes.Figure 4, constructed from our coding/noncoding transcrip-tome quantitation integration by Cytoscape software (Smootet al. 2011), illustrates co-expression of these 26 differen-tially expressed lncRNA genes with the 13 differentiallyexpressed protein-coding genes. In Figure 4, genes thatare closer together are more tightly linked. While it appearsthat there are two linked clusters of coding vs. noncodinggenes, this apparent separation may in fact be due to theoverall lower level of expression of the lncRNAs comparedto the mRNAs (by a factor of almost 80-fold). Some of theselncRNAs, such as NEAT1, are not at or near known coding-gene loci (Figure 3), while others are adjacent to knowngenes that are not differentially expressed. IL8RBP, a com-plex mosaic lncRNA transcript that combines unique up-stream exons with an IL8RB pseudogene downstream exon,

Figure 4 Parallel patterns of activity-dependent coding and noncoding genes.As a means to identify activity-dependentlncRNAs with potential roles in synapticplasticity, we probed the expression pat-terns of 13 known activity-dependentcoding genes against the entire dataset of lncRNAs for parallel patterns ofexpression. This figure shows all signifi-cant relationships between these 13genes and 26 lncRNAs identified usingan R . 0.90 cutoff. Each line representsa significant correlation and the proxim-ity of the genes is directly proportionalto this significance. The length of eachline is inversely proportional to the cor-relation coefficient that is based on theaverage of correlations from probesabove the 0.90 cutoff. The width ofeach line is directly proportional to thenumber of probes above the 0.90 cut-off. Coding genes are shown in bluewhile lncRNAs are pink. This figure wasprepared using Cytoscape (http://www.cytoscape.org).


http://www.cytoscape.orghttp://www.cytoscape.org

is differentially expressed, although its parental gene IL8RBand the RUFY4 known gene that the pseudogene overlaps,are not detectable above background in the same sampleson our protein-coding gene arrays.

Time-dependent patterns of lncRNA–mRNAcodifferential expression with chronic depolarization ofcultured human neuronal cells

To facilitate the study of primate-specific, activity-dependent,coding–noncoding regulatory networks, we developed anin vitro system of repeated depolarization using the humanSH-SY5Y neuroblastoma cell line. Depolarization with supra-physiological concentrations of KCl has been used extensivelyas a means to study CREB activation and downstream tran-scription in neuronal cells in culture (Sheng et al. 1990;Connolly and Kingsbury 2010). Figure 5A shows that, whilea single treatment of these cells with 100 mM KCl leads only

to transient CREB activation (CREB phosphorylation, detect-able by the pCREB Ser106 antibody), repeated 5-min expo-sures with KCl separated by 2-hr intervals lead to moresustained CREB activation, similar to that observed in highlyspiking human neocortex (Beaumont et al. 2012) and in ananimal model of frequent interictal spiking (Barkmeier et al.2012) (Figure 5B).

We then compared gene expression over a 48-hr timecourse together with BDNF and BDNFOS expression byqPCR. Repeated depolarization leads to a marked and moresustained increase in EGR1 activation (a marker of epilepticactivity in the brain) (Figure 5B, bottom panel). Eventhough EGR1 goes up within 4 hr, both BDNF and BDNFOSare initially downregulated. However, whereas BDNF andBDNFOS almost return to baseline levels 24 hr after a singledepolarization, cultures that were repeatedly depolarizedshow a small but significant increase in BDNF expression,

Figure 5 Repeated depolarization in vitro can replicate patterns of coding–noncoding gene transcription. (A) While transient CREB phosphorylation (topand middle) is induced with a single depolarization of Sy5Ycells with 100 mM KCl producing downregulation of BDNF and BDNFOS (bottom), (B)repeated depolarization produces more sustained CREB activation (top), accompanied by a clear oscillation of the pCREB:CREB ratio with pCREBmaximum peaks clearly following each depolarization treatment (middle), and results in a reciprocal pattern of BDNF/BDNFOS transcription at 24 and48 hr (shown by the brace) (bottom). *Reciprocal expression with upregulation of BDNF at 24 hr (P = 0.027) and at 48 hr (P = 0.019) and down-regulation of BDNFOS at 24 ht (P = 0.004) and at 48 hr (P = 0.013) were observed. (A and B, middle panels) Quantification of triplicate Western blots. (Aand B, bottom panels) Triplicate Taqman qRT-PCR results for EGR1 as a positive control for induced activity-dependent transcription, together with BDNFand BDNFOS at each time point. (C) The expression of three cis-encoded lncRNA–mRNA pairs and one known functional lncRNA (NEAT1) was examinedin the same SY5Y repeated depolarization time course as in B, showing multiple distinct patterns of expression of both coding–noncoding pairs and thestand-alone lncRNA.


while BDNFOS remains downregulated at 24 hr. Impor-tantly, the 48-hr time point extends this trend, maintainingthe sustained increase in BDNF expression and demonstrat-ing an even stronger downregulation of BDNFOS. While thisis likely an oversimplification of a dynamic and complex setof regulatory interactions unrelated to BDNFOS, the chronicdepolarization-induced reciprocal BDNFOS depletion andBDNF mRNA increase in culture parallel the inverse rela-tionship between BDNFOS and BDNF in high-activity areasof the human brain shown in Figure 1. Therefore, we ap-plied this cell culture system to interrogate the activity-dependent expression patterns of other lncRNAs and theircis-encoded partner mRNAs from Figure 3.

In Figure 5C, chronic depolarization of SH-SY5Y cellsrevealed a number of distinct time-dependent patterns. Un-like the cis-antisense BDNF/BDNFOS pair, the AF086035-MAPK1P1L cis-antisense pair had an opposite effect ofdecreasing lncRNA and increasing mRNA levels early inthe time course, although by 24 hr the lncRNA level showeda slight increase. The BC013641-ARC neighbor-gene pairdisplayed increased expression of both genes at the 4-hrtime point in the depolarization-treated Sh-sy5y cells, mir-roring the increased expression of both genes in the high-activity human brain. At subsequent time points, ARC mRNAlevels decreased back to the pretreatment levels althoughwe observed a sustained elevated level of the BC013641lncRNA encoded near the ARC gene along the genome (Fig-ure 5C). Since the BC013641 gene is located �6 kb fromARC with a divergent genomic orientation relative to ARCwe designed two custom siRNA oligonucleotides targetingBC013641. Although both siRNAs knocked down BC013641,only one led to a 1.25-fold increase in the mRNA level of theneighboring ARC gene, suggesting that reciprocal gene ex-pression directionality at lncRNA–mRNA pairs may occur atneighbor-gene loci such as BC013641-ARC, and not solely atsense–antisense loci such as BDNFOS-BDNF (data notshown). The BC047792-PURB cis-antisense pair showed in-creased expression of both genes, which mirrored the co-ordinate increase observed in high-spiking brain regions;however, in contrast to BC013641-ARC, expression of bothtranscripts was maximal at the 8-hr time point and returnedto baseline at 24 hr, showing no sustained increase inlncRNA expression. Finally, we looked at the time courseof one stand-alone nuclear lncRNA, NEAT1, which has a po-tentially far-reaching regulatory role. NEAT1 goes up within4 hr, returns to baseline at 8 hr, but shows some chronicelevated expression at 24 hr.

Discussion

A primate-specific lncRNA regulatory mechanismfor BDNF

A striking feature of the BDNF/BDNFOS locus is the com-plexity of its genomic landscape, which is highly representa-tive of the genomic properties observed at lncRNA-encodingloci throughout mammalian genomes. In addition to residing

in a three-gene chain, with LIN7C sharing its bidirectionalpromoter and BDNF overlapping its 39 end, BDNFOS mayhave emerged in recent mammalian evolution after theprimate–rodent divergence. A possible recent origin for thislncRNA gene is supported by two lines of evidence. First,several splice sites of BDNFOS are poorly conserved outsideof primates (data not shown), suggesting that the genomicstructure of BDNFOS either arose or was modified specifi-cally in primate evolution. This is consistent with our recentfinding that lncRNA genes may comprise a majority ofprimate-specific genes (Tay et al. 2009). Second, there is noevidence for a BDNFOS-like gene between the mouse Lin7c andBdnf genes in any public mouse cDNA and EST sequence datarepresented by UCSC Genome Browser transcript-to-genomealignments (not shown); however, a recently identifiednon-orthologous postional equivalent Bdnf antisense tran-script was found in the mouse, suggesting an evolutionarilydistinct, but similar, mechanism for lncRNA-dependent reg-ulation of Bdnf (Engstrom et al. 2006; Modarresi et al.2012). This genomic and evolutionary complexity of theBDNF/BDNFOS locus suggests that functional lncRNAs inthe human brain may be characterized by interspecies non-conservation or high divergence of their gene structures.This is of particular interest because of the persistent inversecodifferential expression of the BDNF/BDNFOS gene pair asa function of human brain activity shown here together withthe observed increase in BDNF mRNA levels followingknockdown of BDNFOS. Our rescue of LIN7C by BDNFOSRNAi indicates that BDNFOS function may, in part, benuclear and epigenetic. This would be consistent with therecent demonstration that an antisense lncRNA acts epi-genetically by modulating target transcription (Tay et al.2009). A possible explanation for the upregulation of bothBDNF and LIN7C via BDNFOS RNAi might involve BDNFOS-mediated PRC2 recruitment to this locus. PRC2 associationwith lncRNAs (Khalil et al. 2009) makes BDNFOS-PRC2binding a distinct possibility. Knockdown of BDNFOS, pre-venting BDNFOS-mediated PRC2 targeting at this locus,would then result in increased LIN7C and BDNF mRNA lev-els, which is what we observed. BDNF is known to be underepigenetic control: it is activated by acetylation of multipleH3 lysine residues in its promoter chromatin (Tian et al.2010) and is repressed in vivo by H3K27Me2 (Tsankovaet al. 2006), a direct PRC2-catalyzed modification (reviewedin Margueron and Reinberg 2011). Our model for BDNFOS-mediated PRC2 recruitment in LIN7C and BDNF suppressiondoes not contradict the concurrent possibility of cytoplasmic,post-transcriptional BDNFOS-BDNF regulation; in fact, theefficiency of our RNAi knockdowns of BDNFOS implies cy-toplasmic localization, since RNA-induced silencing complex(RISC) activity is cytoplasmic. Future work in this fieldshould also clarify whether BDNF mRNA has a reciprocalregulatory impact on BDNFOS.

To determine whether BDNFOS may regulate BDNFtranscription, as opposed to splicing or RNA stability, weperformed Taqman qPCR with a custom amplicon spanning


part of the last exon and part of the last intron of BDNF(hg19::chr11:27,680,112-27,680,229). While there was littlechange in our ability to detect this amplicon with BDNFOSRNAi S1 and S2 treatment, there was an increase with S3,raising the possibility that the BDNFOS effect is at the levelof new transcription (data not shown). However, more workis needed to address the question of whether BDNFOSdirectly regulates BDNF transcription.

In summary, our findings, which center on the poorlyconserved but functional lncRNA BDNFOS, provide a uniquelyhuman view of activity-dependent transcriptional regulatorynetworks in the brain whose endogenous components cannotbe modeled in rodents or other nonprimate species. A set ofdifferentially expressed microRNAs, including miR-30a-5p,act as post-transcriptional inhibitors of BDNF in the prefrontalcortex (Mellios et al. 2008). Our demonstration of the primate-specific lncRNA BDNFOS as an inhibitor of BDNF comple-ments this earlier miRNA work, suggesting that BDNF istargeted by multiple RNA-mediated regulatory mechanismsinvolving short and long, ancient as well as evolutionarilyyoung, noncoding RNAs.

This is the first study where reciprocal lncRNA–mRNA reg-ulation is inferred from the in vivo human brain in a groupwiseanalysis of multiple living patients and then validated byRNAi in a human neuronal cell line. Moreover, reciprocalregulation in sense–antisense pairs is an exception ratherthan the rule (Katayama et al. 2005). Here, we pinpoint anlncRNA, BDNFOS, which, through its ability to regulateBDNF, may be a key novel contributor to epileptogenesis ina locus where future mechanistic analysis is warranted.

Genome-wide integration of coding/noncoding RNAs asa function of human brain activity

Recently, we generated a stringently filtered catalog ofhuman lncRNAs and described the genomic positionalrelationships between these lncRNAs and protein-codinggenes, providing insights into lncRNA functions (Jia et al.2010). Despite their prominence in the transcriptome, mostlncRNAs remain poorly understood, although lncRNAs maycontribute to the biological complexity of regulatory net-works (reviewed in Mattick and Makunin 2006). Becauseof their abundance, lncRNAs may be even more importantthan microRNAs. microRNAs function as post-transcriptionalrepressors, but lncRNAs have additional mechanisms to pos-itively and negatively regulate cotranscriptional and post-transcriptional alterations in gene expression. Here we haveused these insights to develop a custom lncRNA microarrayto provide the first genome-wide analysis of human brainlncRNA-based regulatory networks as a function of electricalbrain activity. Several co-expressed lncRNA/coding gene pairsidentified here have important roles in activity-dependentsynaptic plasticity either directly, such as BDNF and others in-volved in the MAPK/CREB signaling, or indirectly through theexpression of regulatory lncRNAs such as MALAT-1 (Bernardet al. 2010). Our focus on the relationship between cod-ing mRNAs and lncRNAs with respect to brain activity is

complementary to other human brain transcriptome stud-ies such as those focusing on developmental, regional, anddisease-related gene expression patterns (Johnson et al.2009; Voineagu et al. 2011), but significantly expands uponthose studies through our annotation of the human lncRNAtranscriptome and its expression-level relationships withspecific protein-coding genes.

This genome-wide lncRNA expression survey of electricallyactive human neocortex has uncovered hundreds of lncRNAsdifferentially expressed between more and less electrophys-iologically active areas of the human neocortex. Of theselncRNAs, 26 are expressed directly in proportion to knownactivity-dependent genes (Figure 4), and therefore theselncRNAs could represent biomarkers and drug targets for hu-man brain diseases, such as epilepsy (Loeb 2011). The co-expression clustering topology (Figure 4) suggests a networkwhere mRNAs and lncRNAs are linked by previously unchar-acterized lncRNA nodes (such as BC009864) as hubs withspoke edges extending simultaneously to multiple mRNAsand other lncRNAs. We also observed eight lncRNA–mRNAcis-antisense and neighbor-gene pairs characterized by coor-dinated differential expression of both genes in each coding–noncoding pair, suggesting lncRNA-mediated regulation ofprotein-coding gene expression in the brain, and the evenmore intriguing reciprocal possibility that some mRNAs mayfunction at the RNA level to regulate lncRNA expression or inbidirectionally regulated feedback loops in cis. Other lncRNAssuch as NEAT1 were detected only by the trans-regulationanalysis, where we searched for lncRNAs whose expressionwas highly correlated with protein-coding genes regardless ofthe genomic mapping location of those coding genes. Al-though the role of the NEAT2 (MALAT-1) lncRNA from nu-clear speckles in synaptic gene regulation is now known(Bernard et al. 2010), our study complements that work byimplicating NEAT1, the lncRNA from nuclear paraspecklesthat is encoded near the NEAT2 locus, in regulatory interac-tions with activity-dependent genes in the brain. Our threelines of evidence for activity-dependent NEAT1 function inthe neocortex are our detection of NEAT1 as a differentiallyexpressed lncRNA on the custom microarray analysis of hu-man brain samples, our demonstration of activity-dependentNEAT1 expression in depolarized human SY5Y cell culture,and the assignment of NEAT1 as a central node to a co-expression cluster of specific coding and noncoding RNAs(Figure 4). Our cis-regulation and trans-regulation analysesuncovered different, nonredundant sets of lncRNAs, suggest-ing that specific lncRNAs are involved in both types of regu-lation, which for any given lncRNA may be mutually exclusive.These results represent the first functional evidence for a re-markably diverse pattern of lncRNA expression in the hu-man brain.

Functionality of coding/noncoding RNA regulatorynetworks in the human brain

Our microarray results and our qPCR analysis of both theepilepsy patient samples and the recurrent-depolarization


SH-SY5Y cell culture time course jointly represent the firstdemonstration that known lncRNAs are activity-dependentboth in vivo and in cell culture. The complex, but similar,pattern of lncRNA–mRNA expression in activated humanbrain and in a chronically depolarized human neuronal cellline enables the temporal characterization of these regula-tory pathways and provides a new system in which to studythese complex, primate-specific transcriptional regulatorynetworks. We previously performed time-course analysis ofmammalian cis-antisense pairs in cell culture subjected toa specific stimulus, such as lipopolysaccharide induction ofmacrophages, revealing a wide diversity of temporal differ-ential expression patterns. These patterns, with concordantor synergistic regulation of the two paired genes, were ob-served at most of the differentially expressed cis-antisensepairs (similar to our results in Figure 5C). Less frequently,the patterns showed inverse or reciprocal regulation of thetwo paired genes at a locus, similar to our results in Figure2B (Katayama et al. 2005).

Fundamental functional roles have been previously estab-lished for a relatively few lncRNAs. We show upregulation ofthree nuclear RNAs—RNase P (RPPH1), NEAT1, and MALAT-1—in high-activity areas of the neocortex. The catalytic-RNAcomponent of RNAse P is essential for the 39-end cleavage ofboth NEAT1 (Sunwoo et al. 2009) and NEAT2/MALAT-1(Wilusz et al. 2008). Therefore, these three lncRNAs maycompose an lncRNA-mediated lncRNA maturation networkin highly active brain regions (Figure S3 in File S4). Thefunction of this induced network may be to modulate theexpression of synaptic genes, such as those whose mRNAlevels are regulated by MALAT-1 (Bernard et al. 2010). ThisRNA-mediated regulatory network may function either inde-pendently from, or synergistically with, the MAPK/CREBpathway to regulate activity-dependent gene expression.

While BDNF regulation by BDNFOS bolsters previousprecedents for reverse-genetic approaches to functionalvalidation of lncRNA genes (Sheik Mohamed et al. 2010),further mechanistic studies of the novel regulatory lncRNAswill be needed to delineate the functions of these widelyheterogeneous lncRNAs. For example, it is important to dis-tinguish nuclear epigenetic from post-transcriptional and cy-toplasmic regulatory mechanisms of lncRNAs. Such studiesshould be aided by structural insights into the mammalianlncRNAome, following in the footsteps of existing whole-transcriptome empirical RNA secondary structure delinea-tion methodologies (Kertesz et al. 2010). It has also becomeincreasingly evident that human lncRNAs perform diverseyet crucial functions, one of which is to regulate mRNAstability on a transcriptome-wide scale through repetitiveelements embedded in exons of many lncRNAs (Gong andMaquat 2011). Ribonucleoprotein complexes that enablelncRNA function and complexes that facilitate lncRNA-mediated regulation of mRNAs in sense–antisense pairscan be identified by affinity columns and mass spectrometricanalysis. This identification will allow therapeutic targetingof lncRNA–mRNA regulatory relationships. Finally, integrated

differential expression analysis of the protein-coding and thelong noncoding transcriptome represents only a limited en-try point into transcriptional regulatory networks underlyingactivity-dependent gene expression. A comprehensive as-sessment of this network is possible only if all transcriptclasses, including mRNAs, lncRNAs, microRNAs, and the re-cently discovered endo-siRNAs (Smalheiser et al. 2011), areprofiled jointly.

The genomic complexity of the human AK093366-AG2lncRNA–mRNA cis-antisense pair (Figure S5 in File S4) isreminiscent of that observed in the BDNFOS-BDNF lncRNA–mRNA cis-antisense pair. Both the BDNFOS lncRNA gene andthe AK093366 lncRNA gene contained primate-specificsplice sites. The splice donor of AK093366’s sole intron isprimate-specific because it is harbored within an AluJb re-peat. Alu repeats are the best-known class of primate-specific interspersed repeats (Greally 2002), and thereforekey gene structure elements, including splice sites, con-tained within Alu repeats provide direct evidence that thecorresponding gene structures either arose or were modifiedafter the mammalian radiation, specifically in the primatelineage. Notably, EST-supported cis-antisense lncRNA tran-scription of the Alu-containing AK093366 transcriptionalunit extends substantially beyond the UCSC C11ORF96(AG2) gene model and well into the C11ORF96 ORF. Thisunderscores the utility of EST data, much of which remainsunincorporated into reference gene models and annotationsin delineating the boundaries of lncRNA genes, includingthose involved in putative regulatory relationships with pro-tein-coding counterparts. While Alu-containing lncRNAs haverecently been implicated in the in trans post-transcriptionalregulation of gene expression via effecting mRNA decay (Gongand Maquat 2011), our analysis suggests distinct cis-regulatoryroles in overlapping-gene regulation for certain Alu-containinglncRNAs—specifically, AK093366. Two of the four codiffer-entially expressed lncRNA–mRNA cis-antisense pairs in thehuman neocortex, BDNFOS-BDNF and AK093366-AG2, thusfeature primate-specific sequence at lncRNA gene splicejunctions. The protein-coding genes BDNF and AG2 areoverlapped by endogenous antisense lncRNAs containingexonic Alu repeats, and these gene pairs are codifferen-tially expressed in active areas of the human epilepticneocortex.

BDNFOS-mediated regulation of BDNF provides initialevidence that primate-specific regulation of conserved protein-coding genes by cis-antisense lncRNAs takes place in epi-lepsy, a complex human brain disorder. The co-expressionof mRNAs and nonconserved lncRNAs at loci other thanBDNF, including AG2, suggests that primate-specific regula-tion of conserved genes by nonconserved lncRNAs in thehuman brain may not be unique to the BDNF locus.

Acknowledgments

We thank J. B. Brown, Department of Statistics, Universityof California, Berkeley, for helpful comments on the manu-


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script. This work was funded by National Institutes of Health(NIH)/National Institute of Neurological Disorders andStroke grants R01NS045207 and R01NS058802 (J.A.L.)and by internal funds from Wayne State University (L.L.).Microarray development was supported by NIH/NationalInstitute on Drug Abuse grant 1R03DA026021-01 (L.L.).Microarray scanning was performed by the Core Facility ofthe Department of Pediatrics, School of Medicine, WayneState University.

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GENETICSSupporting Information

http://www.genetics.org/lookup/suppl/doi:10.1534/genetics.112.145128/-/DC1

Activity-Dependent Human BrainCoding/Noncoding Gene Regulatory Networks

Leonard Lipovich, Fabien Dachet, Juan Cai, Shruti Bagla,Karina Balan, Hui Jia, and Jeffrey A. Loeb

Copyright © 2012 by the Genetics Society of AmericaDOI: 10.1534/genetics.112.145128

L. Lipovich et al. 2 SI

Files S1 – S3

Supporting Data

Available for download at http://www.genetics.org/lookup/suppl/doi:10.1534/genetics.112.145128/-‐/DC1.

File S1 Dataset of coding mRNA gene expression changes between high and low spiking human cortex for 7 patients. Each pair of samples was measured in quadruplicate using a flip-‐dye design as described in the methods. Those genes reaching significance are indicated as a “1.”

File S2 Dataset of long non-‐coding RNA gene expression changes between high and low spiking human cortex for 7 patients. Each pair of samples was measured in quadruplicate using a flip-‐dye design with 7 unique probes for each gene as described in the methods. Those genes reaching significance are indicated as a “1.”

File S3 Integration of coding / non-‐coding transcriptomes. Here, we determined which of the differentially expressed protein-‐coding genes were encoded by genomic loci overlapping, or adjacent to, the loci which also encoded differentially expressed lncRNA genes as outlined in figure 3a.


File S4

Supporting Figures

Figure S1 Control experiment to rule out non-‐specific effects of siRNA conditions. An siRNA against GAPDH was used on SY5Y cells in order to be certain that non-‐specific or off-‐target effects are not responsible for the results shown in Figure 2 of the manuscript. While GAPDH was significantly knocked down at 24 and 48 hours, there was no effect on BDNFOS, BDNF, or Lin7C. Results shown were each done in triplicate real-‐time qPCR with duplicates at 24 hours and a single sample at 48 hours.


Figure S2 The majority of lncRNAs in our dataset are nonredundant by genomic position relative to lincRNAs and long ncRNAs published by two other groups.


Figure S3 Three nuclear lncRNAs whose levels are increased in more electrophysiologically active areas of the human neocortex form a network: RNAse P is essential for maturation of both NEAT1 and NEAT2 (MALAT1).


Figure S4 Taqman qRTPCR results closely parallel microarray results for lncRNA and mRNA differential expression at lncRNA-‐mRNA cis-‐pairs across the within-‐patient sample pairs of high-‐ and low-‐activity neocortical regions. MALAT-‐1 is not shown because of the discrepancy between its microarray probeset coverage and its Taqman amplicons coverage.


Figure S5 Genomic complexity of the human AK093366-‐AG2 lncRNA-‐mRNA cis-‐antisense pair which is co-‐differentially-‐expressed in human neocortical epilepsy. Red vertical line: lncRNA splice acceptor within an Alu repeat. Magenta arrow: extent of EST-‐supported cis-‐antisense lncRNA transcription overlapping the AG2 protein-‐coding gene. Genomic diagram was generated using the UCSC Genome Browser (Kent 2002).


Files S1 – S3

Supporting Data

Available for download at http://www.genetics.org/lookup/suppl/doi:10.1534/genetics.112.145128/-‐/DC1.

File S1 Dataset of coding mRNA gene expression changes between high and low spiking human cortex for 7 pa

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