RESEARCH ARTICLE

Monoallelic, antisense and total RNA transcription in an in vitroneural differentiation system based on F1 hybrid miceShinji Kondo1, Hidemasa Kato2, Yutaka Suzuki3, Toyoyuki Takada1,4, Masamitsu Eitoku5,Toshihiko Shiroishi1,4, Narufumi Suganuma5, Sumio Sugano6 and Hidenori Kiyosawa1,5,*,‡

ABSTRACTWe developed an in vitro system to differentiate embryonic stem cells(ESCs) derived from reciprocally crossed F1 hybrid mice intoneurons, and used it to investigate poly(A)+ and total RNAtranscription at different stages of cell differentiation. By comparingexpression profiles of transcripts assembled from 20 RNAsequencing datasets [2 alleles×(2 cell lines×4 time-points+2 mousebrains)], the relative influence of strain, cell and parent specificities tooverall expression could be assessed. Divergent expression profilesof ESCs converged tightly at neural progenitor stage. Patterns oftemporal variation of monoallelically expressed transcripts andantisense transcripts were quantified. Comparison of sense andantisense transcript pairs within the poly(A)+ sample, within thetotal RNA sample, and across poly(A)+ and total RNA samplesrevealed distinct rates of pairs showing anti-correlated expressionvariation. Unique patterns of sharing of poly(A)+ and poly(A)−transcription were identified in distinct RNA species. Regulationand functionality of monoallelic expression, antisense transcriptsand poly(A)− transcription remain elusive. We demonstrated theeffectiveness of our approach to capture these transcriptionalactivities, and provided new resources to elucidate the mammaliandevelopmental transcriptome.

KEY WORDS: Antisense, Differentiation, Monoallelic, Neuron,Non-coding, Poly(A)–

INTRODUCTIONThe maternally and paternally derived copies of each gene could begenerally expressed at comparable levels (biallelic expression) indiploid eukaryotic organisms. Expression of certain genes is,however, biased towards that of one of the parents (imprintedexpression) (Barlow, 2011; Bartolomei and Ferguson-Smith, 2011;Lee and Bartolomei, 2013; Savova et al., 2013) or towards that froma specific strain (strain-specific expression) in mice created bycrossing two distinct strains (F1 hybrids), in which one can measure

the expression of genes in an allele-specific manner (Gimelbrantet al., 2007; Eckersley-Maslin and Spector, 2014). There are alsonon-random and random monoallelically expressed genes, whichare neither imprinted nor strain specific (Eckersley-Maslin et al.,2014; Gendrel et al., 2014). Despite intense studies of thesemonoallelically expressed (not meaning an ‘on or off’ situation ofexpression, but rather an ‘allelic bias’ of expression in specificalleles) genes by using F1 hybrids, the regulation and functionalityof them are poorly understood. Although the historical estimate ofthe imprinted gene number was between 100 and 200 (Lee andBartolomei, 2013; Crowley et al., 2015), a range of the numbers,from 95 to 1308, have been reported by recent studies (Gregg et al.,2010a,b; DeVeale et al., 2012; Goncalves et al., 2012; Crowleyet al., 2015). While the discrepancy is partly due to the tissue-specific imprinting expression of many imprinted genes (Lee andBartolomei, 2013), different parental strains of the F1 hybrids, andcoexistence of two subpopulations of cells [one expressing singleand another expressing both alleles of genes within a tissue (Ginartet al., 2016)], it is also caused by concatenation of results reportedfrom unrelated experiments that used different thresholdparameterization and analysis methods. Therefore, investigationover a full developmental time-course from a single experiment mayhelp to determine the number of imprinted genes (Eckersley-Maslinand Spector, 2014).

Although random monoallelic expression has been reported formembers of the olfactory receptor gene family, protocadherins andimmunoglobulins (Cedar and Bergman, 2008; Chen and Maniatis,2013; Magklara and Lomvardas, 2013; Rodriguez, 2013), whole-genome studies have detected unexpectedly large numbers of strain-specific genes in F1 hybrids (Gimelbrant et al., 2005, 2007; Zwemeret al., 2012; Eckersley-Maslin et al., 2014; Gendrel et al., 2014). Inan experiment, for example, of 13,699 expressed genes examined,1666 genes in embryonic stem cells (ESCs) and 1960 genes inneural progenitor cells (NPCs) differentiated from the ESCs showedrandom monoallelic expression in six replicates, whereas 547 genesin ESCs and 276 genes in NPCs showed non-random monoallelicexpression (consistent monoallelic expression in all the sixreplicates) (Eckersley-Maslin et al., 2014). In genes displayingrandommonoallelic expression, one of the two alleles is chosen to besilenced, and this choice is tightly inherited in differentiated cells(Gimelbrant et al., 2005; 2007; Zwemer et al., 2012; Savova et al.,2013). Accurate determination of whether the expression ofmonoallelic genes is specific to developmental stage or constitutivein a single experiment would improve our understanding of theregulation and function of these genes.

Previous studies based on F1 hybrids identified large-scale,dynamic and complex transcriptional activities that involveimprinting, sense–antisense and remarkably different poly(A)+and poly(A)− expression profiles at the Ube3a locus and itsdownstream region extending over more than one megabase (Mb)Received 13 December 2018; Accepted 4 August 2019

1Transdisciplinary Research Integration Center, Research Organization ofInformation and Systems, Tokyo 105-0001, Japan. 2Division of TranslationalResearch, Research Center for Genomic Medicine, Saitama Medical University,Saitama 350-1241, Japan. 3Department of Computational Biology and MedicalSciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba277-8562, Japan. 4Mammalian Genetics Laboratory, National Institute of Genetics,Shizuoka 411-8540, Japan. 5Department of Environmental Medicine, KochiMedical School, Kochi University, Kochi 783-8505, Japan. 6Department of MedicalGenome Sciences, Graduate School of Frontier Sciences, The University of Tokyo,Chiba 277-8562, Japan.*Present address: Department of Life Science, Chiba Institute of Technology, Chiba275-0016, Japan.

‡Author for correspondence (kiyosawa-fks@umin.org)

S.K., 0000-0001-8179-0558; H.K., 0000-0001-7722-5353

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on chromosome (chr)7 (Numata et al., 2011; Kohama et al., 2012;Meng et al., 2012). In mature neural cells, Ube3a gains tissue-specific and maternal-specific expression. Antisense transcripts toUbe3a (Ube3a-ATS) are paternally expressed and [poly(A)–]-predominant [that is, devoid or depleted from the poly(A)+ RNAfraction], and their expression increases with decreasing expressionof maternally expressed Ube3a (Meng et al., 2012). Although thestructure and the border of the transcriptional unit are yet to bedetermined, a 1-Mb transcript, LNCAT (large non-coding antisensetranscript), which starts from the Snurf/Snrpn locus and spans theUbe3a locus in the antisense direction, has been anticipated(Landers et al., 2004; Le Meur et al., 2005). There are other clustersof imprinted genes, including non-coding RNAs with largepolycistronic transcripts in the mouse genome (Lee and Bartolomei,2013; Savova et al., 2013; Luo et al., 2016). Comparison of thearrangements of allele specificities of the imprinted genes, features ofpolycistronic transcripts and binding sites of molecular factorsbetween the imprinted gene clusters will help elucidate theregulation and functionality of these loci. Inspired by the previousfindings, we have developed an in vitro system to differentiate ESCsderived from F1 hydrids into neurons and investigated the dynamicsof monoallelic expression, antisense transcription and bimorphicstatus [expression of both poly(A)+ and poly(A)− transcripts from a

gene locus] comparing poly(A)+ and total RNA transcriptionalactivities on a whole-genome scale during a 17-day time-course ofneural differentiation.

RESULTSRNA libraries, sequencing and bioinformatics analysisF1 hybrid ESCs and mice were obtained in reciprocal crosses ofC57BL/6J (B6) and MSM/Ms (MSM) (Takada et al., 2013) strains(maternal B6 and paternal MSM, termed BM, and maternal MSMand paternal B6, termed MB). ESCs were subject to in vitroneuronal differentiation and sampled at four time-points, day 0 (D0,undifferentiated ESCs), day 4 (D4), day 8 (D8, NPCs) and day 17(N9, neurons) (Numata et al., 2011) (see Materials and Methods).Adult brain (adult, day 70) was included as an in vivo reference tocompare in this study; in figures ‘Adult’was labeled as a time-point.Poly(A)+ and total RNA libraries were strand-specifically preparedfrom each of these samples and sequenced (RNA-seq) withILLUMINA HISEQ sequencer to examine poly(A)+ and totalRNA transcriptional activities of different alleles (B6 of BM, MSMof BM, B6 of MB and MSM of MB, termed B6-BM, MSM-BM,B6-MB and MSM-MB, respectively) (Fig. 1, summary ofabbreviations, threshold parametrization, and terms and definitionsgiven in Table S1). Total RNA, which corresponds more faithfully to

Fig. 1. Experimental setup used in this study. Thecreation of F1 hybrid embryonic stem cells (ESCs),differentiation of the ESCs to neurons and steps ofthe experiment are illustrated in A, B and C,respectively. C57BL/6J (B6) and MSM/Ms (MSM)mice were crossed reciprocally (A), and maleembryo-derived ESCs were differentiated in vitro intoneurons via neural progenitor cells (NPCs) (B). Thecells were sampled at the indicated time-points.Adult mouse brains (Adult) were used as a reference.Poly(A)+ and total RNA libraries were prepared fromeach sample in a strand-specific manner andsequenced in a paired-end condition. The read-pairswere aligned to the mouse genome (mm10) andseparated into B6 and MSM alleles based on SNPs.

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the nascent transcriptome, was used to observe poly(A)− fractionwithin the whole RNA by analyzing in conjunction with poly(A)+data. Hereinafter, we occasionally refer to poly(A)− RNA, based onthe analysis of poly(A)+ and total RNA. Note that our experimentalstrategy lacks a cloning step or single cell resolution, hence all‘monoallelic’ events that occur randomly during differentiation werenot detected.We aligned the RNA-seq sequences [2 cell lines×4 time-points+2

mouse brains=10 datasets for each poly(A)+ RNA and total RNAsample] to the mouse genome (mm10), separated them into B6 andMSM alleles based on SNPs (Takada et al., 2013) to obtain 20sequence datasets for each poly(A)+ RNA and total RNA sample.Reference-guided assembly of the each 20 sequence datasets yieldedtotals of 42,386 and 62,399 non-overlapping expressed [fragmentsper 1 kb of transcript per one million mapped reads (FPKM) ≥1]transcripts (medians of 15,390 and 18,254 at a time-point) inpoly(A)+ and total RNA samples, respectively (Fig. 2, Table S2). Thedetail of bioinformatics analyses are given in the Materials andMethods and Tables S2 and S3.We confirmed appropriate expressionof the differentiation marker genes at each time-point (Fig. 3).

Divergence and convergence of transcript expressionprofiles between the alleles and over the time-course ofneuronal differentiationTo evaluate the influence of cell, strain and parent specificity on theoverall transcript expression profile and the dynamics of thetranscript expression over the developmental time-course, wecompared the expression profiles between two alleles inF1 hybrids from the same reciprocal crosses (intra-allelic pairs:B6-BM versus MSM-BM, B6-MB versus MSM-MB), between

alleles derived from the same inbred strains in different reciprocalcrosses (strain-specific pairs: B6-MB versus B6-BM, MSM-MBversus MSM-BM), and between alleles derived from the sameparent in different reciprocal crosses (parent-specific pairs: MSM-MB versus B6-BM, B6-MB versus MSM-BM) at each time-point(Fig. 4A–D; Table S4). The overall expression profiles differednotably between the alleles, and the level of divergence (orconvergence) changed dynamically over the time-course.

At each time-point, the highest and lowest convergence ofexpression profiles occurred in the strain-specific and parent-specific comparison, respectively (Fig. 4A–D; Table S4), indicatingthat the strain of the chromosome origin is the strongest determinantof the gene expression profile in F1 hybrids. Since there was noconsiderable difference in the convergence in intra-allelic andparent-specific comparisons, the influence of cell or parentspecificity on the expression profile appeared to be marginalcompared to that of strain, at least in the overall expression profile.

The expression profiles were the most divergent at D0, convergedthrough D4 to D8, and showed the tightest convergence at D8 (whencells were committed to differentiation), likely reflecting a stringently

Fig. 2. Numbers of expressed transcripts of the four alleles at the time-points and adult brain. The number of transcripts expressed at an FPKM ≥1were plotted against the four time-points and adult brain for each of the twoalleles of BM and MB strains. The B6-BM, MSM-BM, B6-MB and MSM-MBalleles are indicated in red, blue, green and orange colors, respectively. The leftand right panels show the results for the poly(A)+ and total RNA datasets,respectively. The dotted zigzag line between N9 and Adult time-points indicatesdiscontinuity of time, that is, 17 days and 70 days from undifferentiated ESCs forN9 and adult brain, included as a reference, respectively.

Fig. 3. Expression time-course of developmental marker genes duringESC differentiation and in the adult brain. The expression of the sevendevelopmental marker genes at the expected time-points [A, Nanog at D0, B,Dnmt3a and C, Dnmt3b at D4, D, Pax6 and E, Sox1 at D8, F,Mapt (Tau) at N9and G, Nefh at Adult] in our samples was confirmed by RNA-seq. (A) Theexpression level of the pluripotency-related gene Nanog went quickly downfromD0 to D4 upon differentiation in a synchronized fashion in both reciprocallycrossed ESCs. (B,C) Expression of Dnmt3a and Dnmt3b peaked at D4, theepiblast stage of development. (D,E) Expression of Pax6 and Sox1 peaked atD8 when neural progenitors were enriched. (F,G) Neuron-specific structuralgenes, Mapt (Tau) and Nefh demarcate the maturation levels of N9-cells andthe brain neurons. Although Mapt (Tau) expression was higher in N9-cells,Nefh, whose protein is specifically expressed within the functional axons, wasvirtually absent at N9 and peaked at Adult. This indicates that the N9 neurons,albeit being structurally fully differentiated, might not be as functionally matureas the brain neurons. The absence of glial cells at stage N9 has beenconfirmed in our previous study (Kohama et al., 2012) and here from the RNA-seq data. The x and y axes represent time-points and expression levels,respectively. The B6-BM, MSM-BM, B6-MB andMSM-MB alleles are indicatedin red, blue, green and orange colors, respectively.

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controlled gene expression profile and/or a highly homogenous NPCpopulation. The convergence somewhat relaxed at N9.We confirmed the convergence and divergence of expression

profiles by determining the TPM (transcripts per million) (Wagneret al., 2012) for all the 40 datasets. The correlation between TPMand FPKM, and convergence and divergence reproduced with TPMare shown in supplementary material Fig. S1.

The large-scale, dynamic and complex transcriptionalactivities of Ube3a and its downstream regionConsistent with the previous reports (Numata et al., 2011; Kohamaet al., 2012; Meng et al., 2012), Ube3a became predominantly

expressed from the maternal allele at N9 in both the poly(A)+and total RNA samples (Fig. 5A–C). Based on RNA-seq datasets,we detected four Ube3a-ATS transcripts in the poly(A)+ (p-ATS-1,p-ATS-2, p-ATS-3 and p-ATS-4) and two in the total RNA(t-ATS-1 and t-ATS-2) samples (Fig. 5A–C); all gained paternal-specific expression at N9, and five [three in poly(A)+, and two intotal RNA] were upregulated from N9 to Adult, while protein-coding Ube3a was downregulated from N9 to Adult, consistentwith direct repression of Ube3a with activation of Ube3a-ATS(Huang et al., 2012; Meng et al., 2012). Whereas the signalsof the sense, protein-coding Ube3a transcripts were almostidentical between poly(A)+ and total RNA samples, those of

Fig. 4. Divergence and convergence of expression profiles between the alleles and time-points. To evaluate the level of convergence of expression profilesbetween alleles at each time-point, the expression profiles were compared pairwise between two alleles in the same reciprocal crosses (intra-allelic pairs: B6-BMversus MSM-BM, B6-MB versus MSM-MB), between alleles derived from the same inbred strains in different reciprocal crosses (strain-specific pairs:B6-MB versus B6-BM, MSM-MB versusMSM-BM), and between alleles derived from the same parent in different reciprocal crosses (parent-specific pairs: MSM-MB versus B6-BM, B6-MB versus MSM-BM). The results for poly(A)+ and total RNA are shown in A and B, respectively. The distribution of pairwise ratios ofexpression levels is shown in C [poly(A)+] and D (total RNA). The x axis represents common logarithm of pairwise ratio of expression levels. The data ontranscripts with FPKM≥1 at least in one dataset were used. If the ratios became greater than 5 or lower than−5, they were set to 5 or−5, respectively. The B6-BM,MSM-BM, B6-MB and MSM-MB alleles are indicated in red, blue, green and orange colors, respectively.

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Fig. 5. See next page for legend.

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Ube3a-ATSs were considerably different (Fig. 5B,C). Although theUbe3a-ATS signals were stronger in exons than in introns inpoly(A)+ samples, they spanned the entire Ube3a gene locus,that is exons and introns, in total RNA samples. These resultsindicate the importance of analyzing not only poly(A)+ RNA butalso total RNA to characterize the whole transcriptome, especiallyfor long non-coding RNAs. Paternally expressed antisensetranscripts appeared at D8 in the region downstream of Ube3aand increased at N9 and Adult; these transcripts covered a nearly 3Mb region (59–62 Mb on chr7) (Fig. 5D,E). These transcripts weremostly represented in the total RNA fraction (total RNA-represented), although a substantial proportion was transcribedwith poly(A) tails. Although the number and structures of thesetotal RNA-represented transcripts are yet to be determined,transcript reconstruction through Cufflinks transcript assembly(Trapnell et al., 2010) yielded clearly spliced transcripts in asubstantial unannotated part of the 3 Mb region at N9 and Adult(Fig. S2).

Monoallelically expressed transcripts in poly(A)+ and totalRNA samples over the time-course of neuronaldifferentiationWe identified imprinted and strain-specific autosomal transcripts atthe four time-points and in adult brain for the poly(A)+ and totalRNA samples (threshold parameterization in Table S1B) andfocused on dynamic gain and loss of monoallelic expression. Theexpression biases of monoallelically expressed transcripts and time-course variation are graphically illustrated (Gregg et al., 2010a) inFig. 6A,B. The time-course variation of expression levels of severalconstitutive or developmental-stage-specific monoallelic transcriptsare shown in Fig. 6C,D.We identified 356 imprinted transcripts in poly(A)+ samples

and 631 in total RNA samples (Table S5). The numbers ofimprinted transcripts changed dynamically [43–239 in poly(A)+samples and 12–582 in total RNA samples] over the time-course(Fig. 6E). Most of the imprinted transcripts, in particular intotal RNA samples, were unannotated and paternally expressedin the downstream region of Ube3a. When these transcriptswere excluded, the numbers of detected imprinted transcriptswere 225 in poly(A)+ RNA samples and 158 in total RNAsamples. In agreement with previous studies (Gimelbrant et al.,2005, 2007; Zwemer et al., 2012; Eckersley-Maslin et al., 2014;Gendrel et al., 2014), we found large numbers of strain-specifictranscripts (Table S5): 18,528 in poly(A)+ samples and 6764 in

total RNA samples. The numbers of strain-specifictranscripts also changed dynamically [6336–10,264 in poly(A)+samples and 1079–3321 in total RNA samples] during thetime-course.

We determined whether monoallelic expression wasdevelopmental-stage-specific or constitutive by counting the time-points at which the transcripts showed monoallelic expression(Fig. 6F; Table S5). Most imprinted transcripts [265 (74%) inpoly(A)+ and 515 (82%) in total RNA samples] were imprinted onlyat a single time-point, and 12 (3%) poly(A)+ and 6 (1%) total RNAtranscripts were imprinted at all time-points. Thus, as previouslyknown, imprinted transcripts were highly developmental-stage-specific, although small, but non-negligible, numbers of transcriptsshowed constitutive monoallelic expression (Lee and Bartolomei,2013). Though to a lesser extent, a similar trend was observed forstrain-specific transcripts; 10,006 (54%) poly(A)+ and 4892 (72%)total RNA transcripts were strain-specific at a single time-point, and2032 (11%) poly(A)+ and 406 (6%) total RNA transcripts werestrain-specific at all time-points.

We detected switching between maternal and paternal alleles forthe imprinted genes Grb10 (Charalambous et al., 2003; Garfieldet al., 2011; Cowley et al., 2014; Wilkins, 2014; Ubeda andGardner, 2015; Wolf et al., 2015) and Igf2 (Gregg et al., 2010a),which are known to switch the alleles in different tissues (Fig. 6C,D;Table S5). We also found that 1137 (6.1%) poly(A)+ and 87 (0.2%)total RNA strain-specific transcripts switched the allele between B6and MSM.

Since it is of interest which of activation (or upregulation) of twoactive alleles or silencing (or downregulation) of two inactive allelescaused monoallelic expression, we investigated the change ofexpression levels of the four alleles between two adjacent time-points during neural differentiation in the transcripts which shiftedfrom non-monoallelic to monoallelic expression state. Wecategorized the patterns of the change of expression levels asfollows: (1) all up, all the four alleles increased expression, but todifferent degrees, that is, active alleles upregulated more thaninactive alleles (Fig. S3A); (2) up and same, expression increased inboth active alleles and remained the same, mostly at zero in bothinactive alleles (Fig. S3B); (3) up and down, expression increased inboth active alleles and decreased in both inactive alleles (Fig. S3C);(4) all down, all the four alleles decreased expression, but todifferent degrees, that is active alleles downregulated less thaninactive alleles (Fig. S3D); and (5) others, patterns other than theabove types 1–4 (Fig. S3E).

Although one might expect that monoallelic expression statesof transcripts are created mostly through upregulation ofactive alleles and downregulation of inactive alleles (‘up anddown’ type), the proportion that were of the ‘up and down’ type wasthe smallest (Table S6). The majority of the patterns were of the‘all up’ or ‘up and same’ types. A significant proportion was alsoof the ‘all down’ type, but they were much less abundant thanadditions of the ‘all up’ and ‘up and same’ types. Thus, upregulationof the two active alleles rather than downregulation of the twoinactive alleles contributed more to the transition to monoallelicexpression states in both imprinted and strain-specific transcriptsduring neural differentiation. Note that this categorization of thefive types is an operational one only to capture the core trends ofthe expression changes leading to monoallelic expression states.Since we distinguished monoallelic expression by a threshold-based method evaluating the degree of bias of expression levelsof the four alleles, the monoallelic and non-monoallelic expressionstates are not discrete. Therefore a small change of expression

Fig. 5. Expression profiles of theUbe3a locus and its downstream region.(A) Time-course of the expression of sense and antisense transcripts in theUbe3a locus. The expression levels of each allele of Ube3a and the four andtwo antisense transcripts detected in poly(A)+ and total RNA samples,respectively, were plotted against each time-point. (B,C) Transcription profilesof the four alleles in theUbe3a locus at the four time-points and for adult brain inpoly(A)+ and total RNA samples. Transcriptional activities are represented asthe sequence depth mapped to the Ube3a locus. Positive and negative valueson the y axis represent expression on the positive and negative strand,respectively. Schematic representations of Ube3a and the antisensetranscripts are shown at the bottom. Exons are shown as vertical rectangles.(D,E) Transcriptional activities for Ube3a and its downstream region at the fivedevelopmental time-points. Approximate positions of Ube3a and otherannotated genes are indicated at the bottom. Arrows on the bottomof panels show the direction of transcription. The B6-BM, MSM-BM, B6-MBand MSM-MB alleles are indicated in red, blue, green and orange colors,respectively.

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in an allele from a time-point to another could shift nearlymonoallelic but non-monoallelic transcripts to the monoallelicexpression state. During the categorization of the five types, we

assigned categories even if there was a small change of expressionlevels. Certain portions of the ‘others’ type may be attributed tothese limitations.

Fig. 6. See next page for legend.

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Pairs of expressed sense and antisense transcripts withinand across poly(A)+ and total RNA datasetsWe compared developmental stage specificity, RNA speciescomposition, and positively and negatively correlated variation inexpression over the time-course between three distinct combinationsof sense and antisense transcript pairs: (1) within the poly(A)+datasets [poly(A)+-antisense versus poly(A)+-sense, termed P/Ppairs], (2) within the total RNA datasets (total RNA-antisenseversus total RNA-sense, termed T/T pairs), and (3) between the totalRNA and poly(A)+ datasets (total RNA-antisense versus poly(A)+-sense, termed T/P pairs). Definition of sense and antisensetranscripts is given in Table S1C and Fig. S4. We considered onlypairs of sense and antisense transcripts for which exon(s) of at leastone transcript overlapped exon(s) of annotated transcript(s)(Ensembl version 71) on the same strand. As sense transcripts, weconsidered transcripts whose exon(s) overlapped exon(s) ofannotated transcript(s) on the same strand. Thus, unannotatednovel transcripts whose exon(s) did not overlap exon of anyannotated transcript on the same strand (described in Table S1C andFig. S4) were considered only as antisense transcripts, whereastranscripts whose exon(s) overlapped exon(s) of annotatedtranscript(s) on the same strand were considered to be as bothsense and antisense transcripts. We found 8928 expressed antisensetranscripts in P/P pairs, 7609 in T/T pairs and 8446 in T/P pairs(Table S7). Their numbers varied over the time-course: 5263–6460in P/P pairs, 4860–5846 in T/T pairs, and 5216–6311 in T/P pairs(Fig. 7A; Table S7). Most of the antisense transcripts identified wereunannotated; only 157 (1.8%) of P/P, 149 (2.0%) of T/T, and 173(2.0%) of T/P pairs overlapped annotated antisense transcripts(Ensembl version 71) (Cunningham et al., 2015). Overlapping andnon-overlapping subsets of the antisense transcripts between thethree pair sets are listed in Table S7D. Approximately 40% of theantisense transcripts [41.6% (P/P), 42.9% (T/T), and 40.3% (P/T)]were expressed at all the time-points, whereas more than 20% ofthem [26.2% (P/P), 20.3% (T/T), and 21.7% (T/P)] were expressedonly at one time-point (Fig. 7B; Table S8).We examined the RNA species composition of the expressed

antisense transcripts and expressed sense transcripts [8873 (P/P),6926 (T/T) and 7198 (T/P)] (Fig. 7C; Table S9). The overall

demarcations of the sense and antisense transcripts according to theRNA species were similar. Among the antisense transcripts, thosecorresponding to protein-coding genes were the most abundant inall the three sets (50.2%–54.1%); 11%–12% corresponded to non-coding genes and about 40% were novel transcripts. The highestproportion of novel transcripts (39.9%) was found in the T/P set(37.3% and 35.3% in P/P and T/T sets, respectively). Among thesense transcripts, the proportion of transcripts corresponding toprotein-coding genes was markedly higher in the P/P set (67.4%)than in T/T (62.4%) or T/P set (63.8%).

We were interested in whether the proportions of sense andantisense transcript pairs whose variation in expression over thetime-coursewas positively or negatively correlated differed betweenthe three sets. Although pairs with positively correlated variation inexpression dominated in all the three sets, a significant proportion ofsense and antisense pairs showed highly negatively correlatedvariation in expression (Fig. 7D; notable examples are shown inFig. S5). Interestingly, the T/P set had a significantly higher ratio ofpairs showing negative correlation than the two other sets (χ-squared test P<2×10−4 versus P/P set and <2.7×10−8 versus T/Tset), whereas P/P set had a significantly higher ratio of pairsshowing positive correlation than the two other sets (χ-squared testP<2.2×10−16). The different RNA species enrichment and differentratios of positively and negatively correlated expression in the threesets, may reflect certain differences between the regulatory roles ofdistinct RNA species when they are transcribed as sense andantisense pairs. We also investigated dependence of the correlationof expression variation of sense and antisense transcript pairs onlocation of the overlapped region in transcripts (gene promoter,gene body and 3′ end regions) and the size of exon and intronoverlap. The portion of negatively correlated expression variationdecreased when the overlap was the in promoter and increased whenit was in the gene body and 3′ end (Fig. S6A). In overall, the portionof negatively correlated expression variation decreased with exonoverlap and increased with intron overlap although this trend variedwith distinct ranges of overlap size (Fig. S6B,C).

We found some imprinted antisense transcripts in all the three setsof sense and antisense transcript pairs, some of which were found onthe antisense strand of imprinted sense genes of distinct parentalorigins, as in the Ube3a locus. In addition, 529–1135 (P/P), 49–89(T/T), and 153–308 (T/P) pairs of sense and antisense transcriptsshowed distinct or same mouse-strain specificities (Table S10).

Poly(A)+-predominant, [poly(A)–]-predominant andbimorphic transcripts in various RNA speciesWewere interested in the proportion of poly(A)+ transcripts amongthe total RNA transcripts and how this differed between differentRNA species. A total of 38,404 poly(A)+ transcripts overlapped onthe same strand with 39,754 total RNA transcripts (43,281combinations), whereas 12,609 and 23,071 transcripts wereexpressed exclusively in the poly(A)+ and total RNA datasets,respectively.

Expression levels of the transcripts of the four alleles werecompared between the poly(A)+ and total RNA datasets for each of13 RNA species (Fig. 8A). Clear dominance of total RNAexpression over poly(A)+ expression in histone RNAs (Cui et al.,2010) was confirmed and was used as a positive control for[poly(A)–]-predominant expression (Fig. 8A). The 13 RNA speciesshowed unique proportions of poly(A)+-predominant, bimorphicand total RNA-represented expression. We used the term ‘totalRNA-represented’, as we used total RNA samples. However, totalRNA-represented implies [poly(A)–]-predominant.

Fig. 6. Features of monoallelically expressed transcripts detected overthe time-course. (A,B) Expression biases of the imprinted and strain-specifictranscripts. Expression bias toward the parental chromosomal origin andmouse strain, respectively, of imprinted and strain-specific transcripts wasassessed by comparing the ratios of the number of mapped read-pairs of theB6 allele to that of the MSM allele (B6/MSM). (A) Poly(A)+ samples. (B) TotalRNA samples. (C,D) Time-course variation of expression levels ofrepresentative monoallelically expressed transcripts. Examples ofconstitutively monoallelically expressed (top row), time-point-specific (middle),and allele-switched transcripts (bottom) are shown. In the top andmiddle rows,from left to right, maternally expressed, paternally expressed, B6-specific andMSM-specific transcripts are shown. Bottom rows show transcripts whoseexpression bias switched between maternal and paternal (two left plots) andbetween B6 and MSM alleles (two right plots). (C) Poly(A)+ samples. (D) TotalRNA samples. (E) Dynamic changes in the numbers of monoallelicallyexpressed transcripts over the time-course. The numbers of imprinted andstrain-specific transcripts detected at each time-point are shown. (F)Developmental-stage-specific and constitutive monoallelically expressedtranscripts. The developmental-stage specificity or ‘constitutiveness’ ofmonoallelic expression of transcripts was measured by the number of time-points at which the transcripts showed themonoallelic expression. x and yaxesrepresent the number of time-points at which transcripts showed monoallelicexpression and the number of transcripts, respectively. The B6-BM, MSM-BM,B6-MB and MSM-MB alleles are indicated in red, blue, green and orangecolors, respectively.

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We considered the transcript pairs with the ratio of expressionlevel in total RNA sample to that in poly(A)+ sample [total RNA/poly(A)+] <0.05 and >1.0 as poly(A)+-predominant and total RNA-represented, respectively, and compared their proportions betweenthe 13 RNA species (Fig. 8B). As expected, transcripts from locicontaining non-coding genes showed a significantly higherproportion of total RNA-represented transcripts than those fromprotein-coding gene loci (χ-squared P<10−8), except for processedpseudogenes and processed transcripts. Notably, among the non-coding gene categories, the highest proportion of total RNA-

represented transcripts was found for novel transcripts. There was aclear peak at ∼0.3 in the value for total RNA/poly(A)+ for protein-coding genes and several other RNA species (Fig. 8B). Althoughthis peak at 0.3 implies existence of a certain average ratio ofpoly(A)− to poly(A)+ transcription, whether the ratio reflectsintrinsic amounts of transcripts having a poly(A)+ tail or not is to bedetermined. Although this may partially reflect the complexity oftranscripts, such as length and the number of introns, whichinfluences the processing time until a potential poly(A) tail is added,influences on the numbers of expressed transcripts, distribution of

Fig. 7. Features of antisense transcripts detectedat each developmental stage. (A) Numbers ofantisense transcripts detected at each time-point areshown for the poly(A)+/poly(A)+, total RNA/total RNA,and total RNA/poly(A)+ pairs. (B) Developmental-stage specificity of antisense transcripts. Thenumbers of antisense transcripts expressed at eachtime-point were plotted for the poly(A)+/poly(A)+, totalRNA/total RNA, and total RNA/poly(A)+ pairs.(C) RNA species of sense and antisense transcriptsin the three sets of sense and antisense transcriptpairs. The proportions of the RNA species ofannotated genes (Ensembl version 71) overlappingthe antisense (upper panels) and sense (lowerpanels) transcripts on the same strand are shown.(D) Negative and positive correlation of variation inexpression between sense and antisense transcripts.Pearson correlation was computed for the fiveexpression levels measured at the four time-pointsand adult brain for each sense and antisensetranscript pair in each allele.

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Fig. 8. Comparison of the expression levels of transcripts between poly(A)+ and total RNA datasets. (A) The expression levels of transcriptscompared between poly(A)+ and total RNA datasets for each of the 13 RNA categories. Dotted lines represent y=x−1.74 (y=0.3x in linear scale). The plotfor the antisense category was produced using transcripts of annotated antisense genes. (B) Relationship between transcript expression levels in poly(A)+ andtotal RNA datasets. The frequencies of the ratio of the expression level of transcripts detected in total RNA to those in the poly(A)+ dataset [total RNA/poly(A)+]were plotted for each of the 13 RNA categories. If the ratio of expression levels was less than 0.05, it was set to 0.05. If the ratio was greater than 1, it was set to1.01. (C) The numbers of poly(A)+-predominant (left) or total RNA-represented (right) genomic regions detected in the whole genome, exons, introns andintergenic regions over the time-course. The genomic regions where total RNA expression dominated poly(A)+ expression (P<10−5) at each time-point weredetected by HOMER by comparing the sequence reads mapped on the genome in the poly(A)+ and total RNA datasets. The B6-BM, MSM-BM, B6-MBand MSM-MB alleles are indicated in red, blue, green and orange colors, respectively.

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expression levels and also experimental limitations, includingeffects of amplification during RNA-seq and PCR products incombination with distinct transcript populations between poly(A)+and total RNA samples, cannot be ruled out. These limitations willneed to be addressed in a more-elaborate experimental setup, suchas qPCR designing distinct primers for poly(A)+ and poly(A)−transcripts, in the future.Transcripts from pseudogene loci showed the highest correlation

(Pearson R=0.92) between poly(A)+ and total RNA expression.This may be due to their protein-coding gene origin and may reflecta rather simple regulatory relationship between poly(A)+ and totalRNA expression. Long intergenic non-coding RNA (linc-RNA)transcripts showed lower expression than those of other genecategories for both the poly(A)+ and total RNA samples [Mann–Whitney P<10−8 in poly(A)+ and P<10−7 in total RNA comparedwith protein-coding genes].

Total RNA-represented expression is more dynamic thanpoly(A)+-predominant expressionWe detected genomic regions with total RNA-representedexpression resembling that in the Ube3a locus and also regionswith poly(A)+-predominant expression by using HOMER software(Heinz et al., 2010) and examined their features over the time-course. Total RNA-represented expression changed dynamicallyover the time-course both in the number of transcribed regions anddevelopmental specificity (Fig. 8C). The number of regions withtotal RNA-represented expression remained nearly unchanged fromD0 to N9, but was ∼5-fold higher at Adult. In contrast, the numberof regions with poly(A)+-predominant expression remainedrelatively constant over the time-course. Notably, this surge oftotal RNA-represented regions for the Adult occurred in intronicand intergenic regions rather than in exons (the increase between N9and Adult was 8.2–13.5-fold in introns, 4.4–8.1-fold in intergenicregions and only 2.1–2.8 fold in exons), suggesting greater rolesplayed by unannotated poly(A)− transcripts in mature cells.We found 28 total RNA-represented regions showing imprinted

expression (7 maternally and 21 paternally imprinted) with 5-kb orlarger extensions, including the known clusters of imprinted genesKcnq1 (Mancini-Dinardo et al., 2006; Lee and Bartolomei, 2013)and Meg3-Rian-Mirg (or Dlk1-Dio3) (Luo et al., 2016) loci withpolycistronic transcripts (examples are shown in Fig. S7).

DISCUSSIONWe compared the transcripts of the four distinct alleles in F1 hybridsat distinct developmental time-points and between the poly(A)+ andtotal RNA libraries. By comparing the expression profiles betweenalleles, we found that strain is the strongest determinant of theexpression profile, whereas the impacts of intra-allelic and parentspecificities are both much weaker. The overall expression profileschanged dynamically during neuronal development, with the peakof convergence at D8. In this study, we have shown quantitativelythe relative influence of the alleles and dynamic developmentalconstraints on the overall transcript expression profile.In all the three categories of transcripts (monoallelically

expressed, antisense and total RNA-represented transcripts) weinvestigated, a considerable fraction of transcripts showed highlydynamic expression over the time-course, and a clear demarcationexisted between groups with developmentally specific andconstitutive expression. Although a great majority of imprintedgenes showed developmentally specific expression, a small, butnon-negligible, proportion of them were constitutively expressed.This clear demarcation suggests that there are two categories of

imprinted genes and may suggest certain differences in theirbiological significance (Pauler et al., 2012; Lee and Bartolomei,2013). Strain-specific transcripts showed a similar trend, althoughto a lesser extent. In both monoallelic transcripts (imprinted orstrain-specific), allele switching was very rare. The rarity of alleleswitching was consistent with a previous study (Eckersley-Maslinet al., 2014), which observed coexistence of biased expression ofeither allele among six biological replicates in only one of 1666(0.1%; ESCs) and in 86 of 1960 (4.4%; NPCs) randommonoallelically expressed genes. A large fraction (∼40%) ofantisense transcripts showed constitutive expression, whereas∼20% were developmental stage specific. Total RNA-representedtranscripts showed more dynamic expression variation thanpoly(A)+-predominant transcripts, and poly(A)− transcriptssurged in introns and intergenic regions at Adult. The timing(developmental stage) of expression and its pattern (developmental-stage-specific or constitutive) provide vital information foridentifying the function of specific genes and general functions ofthe three categories of transcripts. We have set a platform foraccurate measurement of the developmental transcriptomicsfeatures by using F1 hybrid ESCs and mice.

Functional generalization of antisense transcripts remainselusive, although intense investigation has been carried out sincethe report of pervasive antisense transcription in the mouse genomeand likeliness of their functionality (Kiyosawa et al., 2003, 2005).Anti-correlation of expression between sense and antisensetranscripts has been observed in individual gene loci, in some ofwhich disruption of the expression of the antisense or sensetranscript affected specific phenotypes (Janowski et al., 2007;Morris et al., 2008; Yu et al., 2008; Watts et al., 2010; Modarresiet al., 2012; Piatek et al., 2017). However, a high-throughputexperiment revealed mostly positive correlation between sense andantisense transcript expression (Cawley et al., 2004). In addition,previous studies have reached a consensus that antisense transcriptsform several heterogeneous groups with different functions (Faghihiand Wahlestedt, 2009; Piatek et al., 2017). The clear demarcation ofdevelopmental-stage-specific and constitutive fractions of antisensetranscripts, the biases of positively and negatively correlatedvariation in expression of sense and antisense transcripts betweendifferent combinations of poly(A)+ and total RNA transcripts, andthe location and size of the transcript overlap, support thisconsensus. At present no categorization can segregate the senseand antisense transcript pairs of negative and positive expressionvariation completely. Additional structural (of the 5′ and 3′ end)(Werner et al., 2009) and functional analyses in conjunction with theproposed mechanisms involving both the 5′ and 3′ end of sensetranscripts (summarized in Piatek et al., 2017) is expected toimprove the demarcation of the positive and negative regulatoryrelationship.

Previous studies have reported that 13.1%–23.3% of mammaliantranscripts are bimorphic (Yang et al., 2011), and 60%–80% areeither poly(A)− or bimorphic (Cheng et al., 2005; Wu et al., 2008).We computed the fractions of poly(A)+-predominant, total RNA-represented and bimorphic transcripts for distinct RNA species andidentified certain rules and trends. First, except for histone RNAs,all RNA species had both poly(A)+ and total RNA-representedtranscripts, and the relative proportions of poly(A)+-predominant,total RNA-represented and bimorphic transcripts showed uniquepatterns for distinct RNA species. Second, six RNA species(protein-coding, pseudogene, linc-RNA, antisense, processedpseudogene and processed transcript) had considerable bimorphicfractions, whereas the remaining five non-coding RNA species were

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[poly(A)−]-predominant. Third, in all RNA species withconsiderable bimorphic transcription, the ratio of the expressionlevel of total RNA to that of poly(A)+ RNA peaked at ∼0.3. Thelandscape of poly(A)+, poly(A)− and bimorphic transcription inmammalian genomes is more complex than previously thought(Mattick and Makunin, 2005, 2006). It was previously found that aconsiderable fraction (24–25%) of transcripts have short or nopoly(A) tails (<20–30 nucleotides) (Gu et al., 1999; Meijer et al.,2007), some of which might be poly(A)+ RNAs processed to reduceor totally remove the classical poly(A) tail (Katinakis et al., 1980).Another study (Yang et al., 2011) identified novel stable poly(A)−intron-derived RNAs and reported a considerable variation in therelative abundance of poly(A)+ and poly(A)− transcription amonggenes for linc-RNAs and protein-coding genes. The non-codingRNAs (or distinct portions of the polycistronic LNCAT) expressedfrom the Ube3a locus showed distinct poly-adenylation rates andhalf lives, thus they were differentially and separately processed(Meng et al., 2012). Further characterization of non-coding RNAs inthese features may reveal a clue as to the roles of non-coding RNAsin imprinting of gene clusters with polycistronic transcripts (Lee andBartolomei, 2013). It is of great interest to investigate how the threemodes of transcription are regulated in distinct RNA species inrelation to their functionality, particularly in imprinted gene clusters.We have described the dynamic gain and loss of monoallelic

expression from the alleles of F1 hybrid ESCs at each time-pointduring neural differentiation. To the best of our knowledge, this isthe first study that investigated the time-course variation ofmonoallelically expressed transcripts over such a long period as17 days in a single experiment. We detected extensive paternalexpression inUbe3a and its downstream region, particularly in totalRNA samples, but the numbers of imprinted transcripts [225poly(A)+ and 158 total RNA transcripts] in all other regions wereconsistent with the historical estimates.The function of genomic imprinting remains elusive, although

theories have been proposed to explain this evolutionary puzzle(Spencer and Clark, 2014), involving ‘kinship theory’ (Haig, 1997;Wolf and Hager, 2006). Recently a new theory has been proposedbased on co-regulation of imprinted genes. Although imprintedgenes appeared to be functionally unrelated, recent studies reportedthat imprinted genes are often co-expressed and potentially make upan imprinted gene network (IGN) which regulates (common)biological processes including extracellular matrix regulation (AlAdhami et al., 2015; Varrault et al., 2017). They also found that amember of the IGN, paternally imprinted Plagl1 (a zinc fingertranscription factor) potentially regulates 22% of 409 genes of theIGN. Plagl1 showed highly dynamic, developmental-specificpaternal expression in our time-course datasets (Fig. S8). Inaddition to Plagl1, we detected four other imprinted transcriptionfactors (TFs), Peg3, Ndn, Zim1 and Tial1 (Crowley et al., 2015) inboth poly(A)+ and total RNA samples (Fig. S8). Similar to Plagl1,Peg3, Ndn and Zim1 are also highly connected with other imprintedgenes in the IGN, thus likely regulate substantial portions of genesin the IGN. The five imprinted TFs showed highly dynamic anddistinct expression profiles (e.g. time span of imprinting) in both ourtime-course datasets (Fig. S8). A network analysis of all thesehighly connected imprinted TFs and their target genes based on theexpression profiles in both poly(A)+ and total RNA samples willrefine the dynamic nature of the proposed IGN.We detected 28 total RNA-represented regions with large

extensions (≥5 kb) and imprinted expression patterns similar tothose of the Ube3a locus, some of which overlapped the knownclusters of imprinted genes with large polycistronic transcripts

(Pauler et al., 2012; Luo et al., 2016). Since non-coding RNAs(products or transcription alone) are an essential element in theregulation of clusters of imprinted genes (Pauler et al., 2012; Leeand Bartolomei, 2013), study of similarities and differences of totalRNA expression features between these loci may bring a clue to theregulation and function of the polycistronic transcripts within theimprinted gene clusters (Luo et al., 2016). We have provided a set of40 expression profiles combining the four alleles, four time-pointsand adult brain, and two RNA libraries. The arrangements ofcis-elements (differentially DNA-methylated regions, enhancers,promoters and insulators) and timing of binding of molecularfactors (TFs, DNA and histone methyltransferases, the transcriptionalinsulator CTCF, the recently found elongation factor AFF3 and yet-to-be-identified factors) to the cis-elements (Hark et al., 2000; Dindotet al., 2009; DeVeale et al., 2012; Luo et al., 2016) responsible for thedynamics and distinctions of the expression profiles need to beidentified in future ChIP-seq experiments.

We demonstrated that our approach is remarkably effective incapturing novel features of the developmental transcriptome. Wehave provided rich resources for mammalian transcriptomics anddevelopmental biology. Similar approaches using our de factostandard for studies on mammalian developmental transcriptomebased on the use of F1 hybrids will expand the research on these andrelated issues of transcriptional regulation in mammalian genomes.

MATERIALS AND METHODSAnimals and mouse ESC linesF1 hybrid mice were bred by crossing B6 mothers and MSM fathers to giveBMmice, and reciprocally crossed to giveMBmice. AMB-ESC line, MB4,was obtained as previously described (Kohama et al., 2012) and designatedas MB-ESC. An additional reciprocally cross-derived ESC line, BM23, wasobtained using the same protocol and designated as BM-ESC. F1 hybridESCs used for RNA-seq were all male. The brains used as references wereexcised from 10-week-old adult male mice. Mouse experiments wereperformed in accordance with the institutional guidelines of KochiUniversity and National Institute of Genetics.

Neuronal differentiation of mouse ESCsHighly efficient neuronal differentiation was performed as previouslydescribed (Kohama et al., 2012) with minor modifications as follows.Undifferentiated mouse ESCs were instead maintained in a KnockOut™Serum Replacement (KSR; Life Technologies)-based medium supplementedwith a MEK-inhibitor (PD0325901; 1 μM) and recombinant mouse leukemiainhibitory factor (|ESGRO; 1000 units/ml). GSK3β-inhibitor, usually addedin the 2i-format, was again omitted here to raise the neuronal differentiationefficiencies (data not shown). The ensuing neuronal differentiation in thechemically defined medium (CDM) was performed exactly as describedpreviously (Kohama et al., 2012) up to the D8 stage. At the neuronalmaturation stage (N0–N9), we instead have supplemented CDM with DAPT(1 μM), CHIR99021 (3 μM) and forskolin (10 μM) to enhance neuronalmaturation and coated the culture dishes with poly-L-ornithine, laminin andfibronectin for enhanced attachment. A step-by-step protocol can be obtainedupon request.

Library preparationTotal RNA was extracted from cultured cells and mouse brains using TRIReagent (Molecular Research Center, Inc.) following the manufacturer’sprotocol. Poly(A)+ and total RNA libraries were prepared using TruSeqStranded kits (Illumina) following the manufacturer’s protocol. Note thatboth poly(A)+ and total RNA libraries were derived from identical samples.

Mapping of ILLUMINA sequencesThe ILLUMINA sequences (paired end, 100 or 101 bp length and 150 bpinsert, 2 cell lines or mice×(4 time-points+adult brain)=10 datasets for eachpoly(A)+ and total RNA sample] were aligned to the mouse genome

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(mm10) by tophat (Trapnell et al., 2009) allowing indels that were six basesor shorter, and fewer than two and five and mismatches in the first 32 basesand in the whole read-length, respectively.

SNPs on the mouse genomeAligning the genomic sequences of MSM (Takada et al., 2013) to the B6genome (mm10), we identified the single-nucleotide polymorphisms(SNPs) on the genomic sequences of mm10. We cut the MSM genomicsequences into 10-kb fragments and the 10-kb fragments were mapped tomm10 genome by using BLAT (Kent, 2002). After selecting only uniquemaps of the 10-kb fragments on mm10, we determined the positions andtypes of SNPs on the coordinates of mm10. A total of 21628229 SNPs(20714172 autosomal and 914057 chrX SNPs) were identified.

Separation and dereplication of the ILLUMINA sequencesmapped to the two allelesBased on the SNPs between the B6 and MSM genomes (Takada et al.,2013), we identified those that originated from the B6 andMSM alleles fromthe mapped read-pairs and separated them into the two distinct alleles. Themapped ILLUMINA sequences were dereplicated based on the mappedpositions. That is, of the read-pairs mapped to the same 5′ and 3′ positions,only one read-pair was taken in each dataset. Since one of our target regions,the Ube3a locus and its downstream region were subject to genomicduplications, we took sequences that were equally mapped to fewer than 100sites on the genome in our downstream analyses. The read-pairs that mappedequally well to two or more sites contributed fractionally (by the inverse ofthe number of mapped sites) to the mapped read-pairs of transcripts and thusto their expression levels.

We could assign medians of 19.0 and 18.0 million (M) read-pairs to B6and MSM alleles, respectively, at each of the four time-points and adultbrain of poly(A)+ datasets, whereas medians of 3.6 and 4.1 M read-pairswere assigned to B6 and MSM alleles, respectively, in total RNA datasets.The number of read-pairs assigned to the two alleles at each time-point forthe poly(A)+ and total RNA datasets are given in Table S2. At this analysisstep, we obtained mapped datasets for the distinct alleles at each of the fourtime-points and adult brain. Thus we had a total of 20 [2 strains×2 alleles×(4time-points+adult brain)] mapped datasets for each poly(A)+ and total RNAsample, a total of 40 mapped datasets for both samples. The downstreamanalyses were carried out independently for each of the 40 mapped datasetsuntil merging the results in each of poly(A)+ and total RNA samples, andnormalizing the expression levels (FPKM) for the 20 datasets of eachpoly(A)+ and total RNA sample.

Reference-based assembly of transcripts based on thesequences mapped to the genomeBy using Cufflinks (Trapnell et al., 2010; http://cole-trapnell-lab.github.io/cufflinks), we assembled the expressed transcripts based on the coordinatesmapped by the ILLUMINA sequences on the genome using the exoncoordinates of Ensembl database (version 71) (Cunningham et al., 2015) asa reference gene set for each mapped dataset. This assembly was performedfor each of the 40 mapped datasets. Thus we obtained 40 sets of transcriptassemblies (gtf files) after this step.

Merging of the transcript assemblies and computation of thenumber ofmapped read-pairs and normalized expression level ofeach transcriptBy using cuffmerge (http://cole-trapnell-lab.github.io/cufflinks), we mergedthe 20 assemblies and obtained a single merged assembly for each poly(A)+and total RNA sample. By using cuffdiff (http://cole-trapnell-lab.github.io/cufflinks) [comparing mapping results of the 20 sequence datasets with thesingle merged set of assembled transcripts in each poly(A)+ and total RNAsample], we computed read-pair counts and expression levels (FPKM)normalized over the 20 mapped datasets for the assembled transcript in eachpoly(A)+ and total RNA sample. We tested classical (no scaling) andgeometrical (FPKMs are scaled via the median of the geometric means ofmapped read-pair counts) (Anders and Huber, 2010) normalization andfound the later superior to maintain the profile of expression levels betweenthe B6- and MSM-alleles between each paired mapped datasets. Thus, we

used the geometrically normalized expression levels for the comparison ofexpression levels between poly(A)+ and total RNA datasets over the time-course.

At this step, we detected totals of 42,386 and 62,399 non-overlappingCufflinks-defined genes that showed an expression level with a FPKM≥1.0in at least one of the 20 datasets (one allele at a time-point) for the poly(A)+and total RNA samples, respectively. 15,327 (37.0%) and 15,674 (25.1%)of the Cufflinks-defined genes overlapped known genes annotated in theEnsembl database in poly(A)+ and total RNA samples, respectively [seeTable S3 for a summary including numbers at a lower (≥0.5) FPKMthreshold]. The Cufflinks-assembled genes mapped 16,684 and 16,769Ensembl genes on the same strand in poly(A)+ and total RNA samples.16,555 (99.2%) and 16,690 (99.5%) of the Ensembl genes were mapped bya single Cufflinks-defined gene in poly(A)+ and total RNA samples,respectively, whereas in the remaining portions, 129 (0.8%) and 80 (0.5%)were mapped by two to four and two to five Cufflinks-defined genes inpoly(A)+ and total RNA samples, respectively. As transcriptional units, weused the Cufflinks-defined genes in downstream analyses. We operationallyused the term ‘transcript’ as a Cufflinks-defined gene since there was noreference for gene locus determination in unannotated regions of thegenome.

All the figures were plotted by using R (R Core Team, 2014). All thecomputational work was performed on the DDBJ supercomputer system(Ogasawara et al., 2013).

Competing interestsThe authors declare no competing or financial interests.

Author contributionsConceptualization: H. Kiyosawa; Methodology: H. Kato, H. Kiyosawa; Software:S.K.; Validation: S.K., H. Kiyosawa; Formal analysis: S.K., H. Kato, H. Kiyosawa;Investigation: S.K., H. Kato, Y.S., T.T., M.E., T.S., N.S., S.S., H. Kiyosawa;Resources: H. Kato, Y.S., T.T.; Data curation: S.K., T.S., N.S.; Writing - original draft:S.K., H. Kato, M.E., H. Kiyosawa;Writing - review & editing: S.K., H. Kato, M.E., S.S.,H. Kiyosawa; Visualization: S.K.; Supervision: H. Kiyosawa; Project administration:H. Kiyosawa; Funding acquisition: H. Kiyosawa.

FundingThis work was supported in part by Japan Society for the Promotion of Science(JSPS) KAKENHI Grant-in-Aid for Scientific Research (B) (No. 26290063), Ministryof Education, Culture, Sports, Science and Technology Japan (MEXT) KAKENHIGrant-in-Aid for Scientific Research on Innovative Areas (No. 24115703) andMinistry of Education, Culture, Sports, Science and Technology-Japan (MEXT)KAKENHI (No. 221S0002) to H. Kiyosawa.

Data availabilitySequence datasets were deposited to DDBJ under the accession numbersDRA003816 and DRA003817. The 40 sgr files to generate expression tracks of thefour alleles at four time-points and adult brain for poly(A)+ and total RNA samples areavailable upon request.

Supplementary informationSupplementary information available online athttp://jcs.biologists.org/lookup/doi/10.1242/jcs.228973.supplemental

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