decoding of exon splicing patterns in the human runx1–runx1t1

7/17/2019 Decoding of Exon Splicing Patterns in the Human RUNX1RUNX1T1

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The International Journal of Biochemistry& Cell Biology 68 (2015) 4858

Contents lists available at ScienceDirect

The InternationalJournal ofBiochemistry& Cell Biology

journa l homepage: www.elsevier .com/ locate /biocel

Decoding ofexon splicing patterns in the human RUNX1RUNX1T1

fusion gene

Vasily V. Grineva,, Alexandr A. Migasb, Aksana D. Kirsanavaa, Olga A. Mishkovab,Natalia Siomava c, Tatiana V. Ramanouskaya a, Alina V. Vaitsiankova a, Ilia M. Ilyushonaka,Petr V. Nazarov d, Laurent Vallar d, Olga V. Aleinikovab

a Department of Genetics, Faculty of Biology, Belarusian StateUniversity,Minsk, Belarusb Laboratory of the Genetic Biotechnology, Department of Research, Belarusian Research Center for Pediatric Oncology, Hematologyand Immunology,

Minsk, Belarusc Department of Developmental Biology, University of Gttingen, Gttingen, Germanyd Genomics Research Unit, Luxembourg Institute of Health, Luxembourg

a r t i c l e i n f o

Article history:

Received 1 May2015

Received in revised form 12 August 2015

Accepted 24 August 2015

Available online29 August 2015

Keywords:

RUNX1RUNX1T1 fusion gene

Alternative splicing

Datamining

Exons-hubs

Power-lawbehavior

a b s t r a c t

The t(8;21) translocation isthemostwidespreadgenetic defect found in humanacutemyeloid leukemia.

This translocation results in the RUNX1RUNX1T1fusion gene that produces awidevariety ofalternative

transcripts andinfluences thecourse ofthedisease. Therules ofcombinatoricsandsplicingofexons in the

RUNX1RUNX1T1transcripts are not known. To address this issue,wedevelopedan exongraphmodel of

the fusion gene organization and evaluated its local exon combinatorics bythe exon combinatorial index

(ECI). Herewe show that the local exoncombinatoricsofthe RUNX1RUNX1T1gene followsa power-law

behavior and (i) the vast majority ofexons has a low ECI, (ii) only a small part is represented by exons-

hubs ofsplicingwith very high ECI values, and (iii) it is scale-freeandvery sensitive to targeted skipping

of exons-hubs. Stochasticity of the splicing machinery and preferred usage of exons in alternative

splicing can explain such behavior ofthe system. Stochasticitymay explain up to 12% ofthe ECI variance

and results in a number ofnon-coding and unproductive transcripts that can be considered as a noise.

Half-life of these transcripts is increased due to the deregulation of some key genes of the nonsense-

mediated decay system in leukemia cells. On the other hand, preferred usage ofexons may explain up

to 75% of the ECI variability. Our analysis revealed a set of splicing-related cis-regulatory motifs that

can explain attractiveness ofexons in alternative splicing but only when they are considered together.

Cis-regulatorymotifs are guides for splicing trans-factors andwe observed a leukemia-specific profile of

expression ofthe splicing genes in t(8;21)-positive blasts. Altogether, our results show that alternative

splicing of the RUNX1RUNX1T1 transcripts follows strict rules and that the power-law component of

the fusion gene organization confers a high flexibility to this process.

2015 Elsevier Ltd. All rights reserved.

1. Introduction

Thet(8;21)translocation occursin 412%of adultand1230%of

pediatriccasesof acutemyeloid leukemia(AML)and representsthe

most common genetic abnormality in human leukemias (Mller

et al., 2008). The main outcome of the translocation is the fusion

gene RUNX1RUNX1T1, which produces a wide range of different

transcripts (Era et al., 1995; Erickson et al., 1992; Kozu et al., 1993,

Correspondingauthorat: Department ofGenetics,Facultyof Biology,Belarusian

State University, Nezavisimosti Avenue 4, 220030Minsk, Belarus.

E-mail address: grinev [email protected] (V.V. Grinev).

2005; LaFiura et al., 2008; Lasa et al., 2002; Mannari et al., 2010;

Miyoshi et al., 1993;Nissonet al., 1992;Saunderset al., 1996;Tighe

and Calabi, 1994; Van de Locht et al., 1994; Yan et al., 2006; Zhang

et al., 1997). One part of these transcripts is protein-coding, the

other is non-coding. Both full-length and truncated isoformsof the

fusionprotein were also found experimentally. These isoforms are

transcriptional regulatorswithdifferent activity (Kozuet al., 2005;

LaFiura et al., 2008; Mannari et al. , 2010; Yan et al., 2006). It is

believed that RUNX1RUNX1T1 proteins play the critical role in

the initiation and persistence of the t(8;21)-positive AML (Hatlen

et al., 2012).

A large diversity of the RUNX1RUNX1T1 transcripts raises a

question if there is any rule of exon combination and splicing. To

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V.V. Grinev et al./ The International Journal of Biochemistry& Cell Biology 68 (2015) 4858 49

date, only some elementsof this puzzleare known.Thus, Tigheand

Calabi (1994) showed that thestructureof thebreakpoint regionof

the fusion gene influences variety of its transcripts. LaFiura et al.

(2008) found a connection between inclusion of cassette exons

from this region and formation of premature termination codons

(PTCs) in transcripts. At the same time, usage of some other alter-

native exons does not lead to a PTC but produces active isoforms

of the protein (Mannari et al., 2010; Yan et al., 2006). However,

availabledata areinsufficientfor thefullunderstandingof thesplic-

ingprinciplesof theRUNX1RUNX1T1transcripts.Meanwhile, this

knowledge would allow us to clarify organization of the fusion

gene, its properties and functional role in leukemogenesis.

Our goalwas tofindoutwhether there isanypattern in the local

exon combinatorics of thefusiongene. In this article, theterm local

exon combinatorics refers to a set of alternative splicing events

generating different mRNA isoforms from a given exon, whereas

exon combinatorial index (ECI) is a quantitative measure of the

localexoncombinatorics. Insteadof the conventional linearmodel,

we used an exon graph model of the fusion gene inwhich the ECI

is an equivalent of the topological index node degree and means a

number of unique splicing events that involve an exon.

Here we show that empirical distribution of ECI values of the

RUNX1RUNX1T1 exons follows a power-law function and has

some specific properties: the vast majority of exons has a low ECI

while a small part is represented by exons-hubs of splicing with

high ECI values, the distribution is scale-free and is sensitive to

targeted skipping of exons-hubs. This distribution is formed by

stochasticityof thesplicingmachineryandpreferredusageof exons

in alternative splicing, where attractiveness of an exon is mostly

determined by a set of sequence-related features. Altogether, our

results show that alternative splicing of the RUNX1RUNX1T1

transcripts follows strict rules and that the power-lawcomponent

of the fusion gene organization confers a high flexibility to this

process.

2. Materials and methods

2.1. Cell line, patients and healthy donors samples

The t(8;21)-positive AML cell line Kasumi-1 (ATCC CRL-

2724TM) was obtained from the ATCC (LGC Standards GmbH,

Germany) and cultivated according to the standard protocol.

Twelve young patients with t(8;21)-positive AML were

diagnosed and treated at Belarusian Research Center for Pedi-

atric Oncology, Hematology and Immunology (Minsk, Belarus).

Mononuclear cellswere isolatedusingHistopaque (SigmaAldrich,

StLouis,USA) frompatientsbonemarrowsamplesobtainedbefore

the treatment and/or at the time of remission.

Bonemarrowmononuclear cells(BMMNC)andperipheralblood

mononuclear cells (PBMNC) were obtained from primary material

of healthy donorsusingHistopaque (SigmaAldrich, St Louis,USA).

CD34+ hematopoietic progenitor/stem cells (HPSC) were isolated

from BMMNC of healthy individuals by magnetic separation with

EasySepHumanCD34PositiveSelectionKit (StemCellTechnologies

SARL,Grenoble,France).Forthefurther totalRNAisolation,weused

only cell samples with purity of CD34+ HPSC99%.

This studywasapproved by the institutional ethical committee

andourresearch team followed theprinciples of theDeclaration of

Helsinki for research involvinghuman subjects.

2.2. cDNA synthesis, standard RT-PCR and real-time PCR

Total cellular RNA was isolated from cells using a TRI Reagent

(SigmaAldrich, St Louis, USA) according to the instruction of the

manufacturer.

For cDNA synthesis, we used 1g of total cellular RNA in the

final reaction volume of 20l with Oligo-dT and SuperScript III

Reverse Transcriptase Kit (Life Technologies, Carlsbad, USA). PCR

was performedwith Platinum TaqDNAPolymerase Kit (Life Tech-

nologies,Carlsbad,USA), 0.30.5Mofeachprimerand2l oftotal

cDNA as a template. Real-time PCR was performed in duplicates

on StepOnePlus Real-time PCR System (Life Technologies, Foster

City, USA) using QuantiTect SYBR Green PCR Kit (Qiagen GmbH,

Hilden, Germany) in 12.5l volume with 0.3

M of each primer

and 1l of diluted 1:2 total cDNA (final dilution 1:25) as a tem-

plate. Abundance of target transcripts was normalized relative to

the expression level of the TBP gene, coding a TATA box binding

protein, as previously described (Migas et al., 2014) and quantified

according to theRuijters approach (Ruijter et al., 2009).

2.3. cDNA library

Single stranded cDNA from leukemia blasts was converted

into double-stranded cDNA and amplified by primers specific

to annotated 5UTRs and 3UTRs of the RUNX1RUNX1T1 gene

in standard PCR. PCR products were ligated into the pTZ57R/T

cloning vector (ThermoScientific, Lithuania) that was used for the

further transformation of XL1-Blue Escherichia coli strain. Recom-binant DNA was purified from the positive clones and sequenced.

Obtainedsequenceswerealignedagainsthumanreferencegenome

GRCh37/hg19 by BLAT (Karolchik et al., 2014), exon structure of

transcriptswasdescribed,and newvariantsweredepositedin Gen-

Bank (Supplementary Table 1).

2.4. DNA gel-electrophoresis, purification and sequencing

Amplicons were separated in 12% agarose gel and extracted

withQIAquickGelExtraction Kit (QiagenGmbH,Hilden,Germany).

Capillary DNA gel-electrophoresis was performed with Agilent

2100 Bioanalyzer (Agilent Technologies, Santa Clara, USA) using

DNA 7500 Kit according to the protocol of the manufacturer.

Sequencing reaction was performed using BigDye v3.1 Ter-minator Cycle sequencing Kit (Applied Biosystems, Austin, USA).

Products of the reaction were cleaned up with ethanol precipita-

tion andanalyzedon 3130 Genetic Analyzer (Hitachi,Tokyo, Japan)

according to the standard procedure.

2.5. Exon graph reconstruction and manipulations

Exon graph of the RUNX1RUNX1T1 gene was reconstructed

according to the previously described approaches (Heber et al.,

2002;Majoros etal., 2014). TheECIs,the shortestdistancesof exons

in an exon graph, Kleinbergs authority scores and the assortativ-

ity coefficient were calculated by R/Bioconductor package igraph

v.0.6.5-2 (Csardi andNepusz, 2006).

Kleinbergs authorityscore is a local topological index that indi-cateswhether there isa tendency forsplicingof exonswithhighECI

values together (Kleinberg, 1999; Newman, 2003). Potential clus-

tering of exons by Kleinbergs authority score was evaluated by

k-meansmethod implemented in R.We used Akaike and Bayesian

information criteria to identify theoptimal numberof clusters and

R/Bioconductorpackage ConsensusClusterPlusv.1.22.0 (Wilkerson

andWaltman, 2015) with 1000 subsamples to investigate thecon-

sensus between the clusters.

The assortativity coefficient is a global characteristic of an exon

graph (Newman, 2002). If this coefficient is 1, the graph is per-

fectly assortativeandexons stronglyprefer splicingwiththe similar

exons (in terms of ECI values). Otherwise, when the coefficient is

1, the graph is completely disassortative and exons with high

ECI values are spliced with exons with low ECIs and vice versa.


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50 V.V. Grinev et al. / The International Journal of Biochemistry& Cell Biology 68 (2015) 4858

Finally, in the absence of any preferences for splicing, the graph

is non-assortative and the coefficient is 0.

2.6. Fitting of statistical models to empirical data

Initially, we rejected those statistical models that clearly do

not fit the empirical distribution and obtained five closest mod-

els: power-law, power-lawwith exponential cut-off, exponential,

stretchedexponential (orcomplementarycumulativeWeibull)andlog-normal distributions.Then,we fittedselectedstatisticalmodels

to theempirical distributionaccording to xmin paradigm (Clauset

et al., 2009). Finally, goodness-of-fit test, log-likelihood ratio test,

KolmogorovSmirnov test and Akaike and Bayesian information

criteriawereused toassessthe plausibilityof thestatisticalhypoth-

esis and for the direct comparison of alternative statistical models

(Clauset et al., 2009; Klaus et al., 2011; Vuong, 1989).

2.7. Identification of the significant open reading frames (ORFs)

and PTC in transcripts

Primarily, all possible ATG-ORFswere identified in transcript(s)

of interest. Next, for each empirical transcript, 100 random

sequences with the same length were generated using a multi-nomial model (Ababneh et al., 2006). This new set of artificial

transcripts was used to identify of ORFs. Finally, 99th percentile

of the distribution formed by lengths of artificial ORFs was used

as a threshold for identification of the true ORF(s) in the empiri-

cal transcript. Transcripts with no significant ORFs were classified

as non-coding. To identify PTCs, exonic structure and coordinates

of ORF(s) in the transcript of interest were matched. A transcript

wasannotatedasPTC-containing if theendof itsORFwaslocalized

upstream of the last exonexon junction in the transcript.

2.8. Development of a short-list of the most important

nonsense-mediated decay (NMD) and splicing genes

We used a three-step approach to select genes into a short-list.First, wedownloadedmicroarraydatafor t(8;21)-positiveAMLand

normal hematopoietic cells (Supplementary Table 2) from NCBI

GEO repository (Barrett et al., 2011). We used this set of microar-

rays for two-classdifferentialgeneexpression analysiswith limma

v.3.22.1 (Smyth, 2005) and selected genes with at least 2-fold sta-

tistically significant difference in expression.Second, we useda set

of differentially expressed genes from the first step and leukemia

microarray data to reconstruct a gene regulatory network with

ARACNE2 algorithm (Margolin et al., 2006). For genes from this

network, we calculated combined centrality scores (del Rio et al.,

2009). Finally,we functionallyannotatedtop-scoredgenesfromthe

secondstep andselectedonly hub-like entities into thefinal short-

list. This three-step approach allowed us to focus only on the most

interesting NMD and splicing genes and to verify their differentialexpression by real-timePCR in limited clinical material.

2.9. Data mining by regression random forests

All important sequence features were selected with Boruta

v.3.1.0 (Kursa and Rudnicki, 2010). Machine learning was carried

outwith package randomForest v.4.6-7 (Breiman et al., 2013; Liaw

and Wiener, 2002) in regression forests mode for nonlinear mul-

tiple regression. Importance of each feature was determined via

calculation of the mean decrease in accuracy of ECI value predic-

tion after randompermutation of theoriginal valuesof thefeature.

Accuracyof thepredictionwasevaluatedbySpearmans between

empirical and predicted values of the ECI and by the coefficient of

determination. For integrated representation of the complex data,

an implementation of Circos plot in R package circlize v.0.2.5 was

used (Gu, 2015; Krzywinski et al., 2009).

2.10. Modeling of the exon skipping

For this kindof analysis, weused a general approach developed

by Trajanovski et al. (2013). Theoretically expected exon graph-

generated transcripts were identified with the full crawl of the

graph. In order to produce stable and reproducible results, 1000simulations weremade for each fraction of the skipped exons.

3. Results

3.1. The RUNX1RUNX1T1 gene is a source of unprecedented

diversity of mRNA products

Toreconstruct theexon graph,we created a comprehensive col-

lection of transcripts of the fusion gene. We identified 102 unique

full-lengthtranscriptsand8uniqueexpressedsequence tags(ESTs)

of the gene of interest in PubMed, GenBank and ChimerDB 2.0

databases(Bensonet al., 2013;Era et al., 1995;Ericksonet al., 1992;

Kim et al., 2010; Kozu et al., 1993, 2005; LaFiura et al., 2008; Lasa

et al., 2002; Mannari et al., 2010; Miyoshi et al., 1993; Nisson et al.,1992; Saunders et al., 1996; Sayers et al., 2012; Tighe and Calabi,

1994; Van deLocht et al., 1994; Yan et al., 2006; Zhang et al., 1997).

In these sources, exon structure of all transcripts was described,

but the nucleotide sequence of some rare and unique exons was

not published. Therefore, we were able to fully reconstruct the

nucleotide sequence for 61.8% of full-length transcripts, and the

sequence of remaining transcripts was restored only partially.

To complete our collection, we created a cDNA library. The

library is based on cDNA from bone marrow samples of 12 young

patients with t(8;21)-positive AML(Supplementary Table 3) and

Kasumi-1 cells. For cDNA amplification, we used forward primers

directed to5UTR exons 1, 4a/4b, 7a/7c, 7d, 8a and 11a and reverse

primers directed to 3UTR exons 12a, 15a, 17a and 17of the fusion

gene. We also used primers specific to internal exons to amplifyrare andpoorly detected transcripts (Fig. 1; Supplementary Tables

4 and 5).

In our cDNA library, we identified 33 new full-length and 55

short EST-like transcripts (Supplementary Table 1). This helped us

to expand significantly the list of known transcripts of the fusion

gene: current collection includes 135 full-length and 63 EST-like

sequences. From 55 newly found ESTs, 30 sequences matched the

full-length transcripts of the fusion gene only partially. It means

that in t(8;21)-positive leukemia exists a subset of rare or hardly

amplified full-length transcripts that were not identified so far.

3.2. Power-law behavior of the local combinatorics of the

RUNX1RUNX1T1 exons

To find out the character of the local exon combinatorics, we

developedan exongraphof thefusion gene organization. This exon

graph is based on full-length transcripts and includes 99 exons

connected by 163 splicing events (Fig. 2A).

We quantified the exon usage in different alternative splicing

events by the exon graph topology analysis and expressed this

metric with ECI values. This index falls in the range from 1 to 34

with high standard deviation of 5.1. Visual inspection of the ECI

value distribution lead us to thehypothesis that this index follows

a power-law function. To test this hypothesis, we used a three-

step approach (Section 2) based on the mathematical formalism

of (Clauset et al., 2009; Virkar and Clauset, 2012). Our statistical

tests supported the power-law model y=x2.31 of the observed

distribution (Fig. 2B).


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Fig. 1. A set of primers specific forterminal or internal exons of human RUNX1 andRUNX1T1 geneswasused forthe RUNX1RUNX1T1 gene cDNA library construction.

Analysis of the cumulative distribution shows that approxi-

mately 80%of exons of theRUNX1RUNX1T1gene have a small ECI

value 3. This exon group represents cassette (mainly UTR exons

and exons from the breakpoint region) and constitutive (most of

the exons from3-RUNX1T1part of the fusion gene) exons that arenot involved in alternative splicing.

At the same time, about 20% of the remaining exons have high

combinatorial index 4. These exons form a heavy right tail of

the empirical distribution. They are constitutive and are widely

used in alternative splicing as the exons of this group account

for about 80% of the total diversity of splicing events occurred in

the fusion gene transcripts. Noteworthy, exons 5, 6, 8b, 9, 10 and

11 are the most interesting: about 64% of the diversity of splic-

ing events occurs involving these exons. Herewith, exons 5 and 6

encode almost entire DNA binding Runt homology domain RHD

of the RUNX1RUNX1T1 protein (Meyers et al., 1993) and exon 8

encodes a polypeptide bridge that connects RUNX1 and RUNX1T1

parts of the fusion protein. As for exons 9, 10 and 11, they encode

the first conservative domainNHR1 from the RUNX1T1 part of the

RUNX1RUNX1T1 protein (Davis et al., 2003).

To clarify the relationship between the two groups of exons

mentionedabove,weevaluatedsplicingpreferencesof theseexons

byKleinbergs authority score and theassortativity coefficient.Wefoundthataccording totheauthorityscore allexonscanbegrouped

into three stableclusterswith consensushigher than 0.95.Thefirst

cluster includedexonswithextremelylowauthorityscorebetween

4.4e18 and 5.4e2 (dark-green balls, Fig. 2C), the second clus-

terwas composed of exonswith moderate authority score ranging

from6.6e2 to0.3 (redballs, Fig. 2C) and, finally, theoutlyingexon

8b was always considered as the third cluster (blue ball, Fig. 2C).

Herewith, the second cluster is represented by exons with ECI val-

ues ranging from 2 to 31 (mean 4.4) that is on average 2.1 times

higher (p=0.0006, MannWhitney U test) than for exons of the

first cluster withECI values ranging from1 to9 (mean 2.1). Despite

this, theassortativity coefficient for thewhole exon graph is0.38,

which is apparently duetoa significantpredominance of theexons

Fig. 2. Thelocalcombinatorics of RUNX1RUNX1T1 exons follows a power-lawbehavior. (A)Exongraphof theRUNX1RUNX1T1gene.Exonswereclustered into 23 groups

(E) based on the genomic origin and/or overlapping of sequences. For each group, a well-known reference exon is shown in parentheses. (B) The power-lawbehavior of

the local combinatorics of RUNX1RUNX1T1 exons is supported by statistical tests on plausibility. The power-law function is good fitted (red dashed line) to the heavy

right tailof empirical data (bluediamonds)and has the lowest KolmogorovSmirnovdistanceD and thehighest bootstrapp-value among competing statistical models (see

Section 2). (C) Exons can be grouped into three stable clusters based on Kleinbergs authority score. However,most of exons have extremely lowor moderate values of the

authority score.(For interpretation of thereferences to color in this figurelegend, thereader is referred to theweb version of this article.)


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1.00.80.60.40.20.00.0

0.2

0.4

0.6

0.8

1.0

Cum

ulativeprobability

Normalized ECI value

1

43

2

420-2-40

10

20

30

40

ECIvalue

Position of exon in transcripts

A B

Fig. 3. Stochastic noise in the splicing machinery and thepositional distribution of exons in transcripts make a minor contribution to the variance of ECI values. (A) There

is a clear and significant difference between thecumulative curve of theempirical ECI values(line 1) and thetheoretical cumulative curve fora randomexon graph (line 2)

(p=1.4108 , MannWhitneyU test). No significant difference was found between empirical andnon-coding (line 3) or unproductive (line 4) noise corrected cumulative

distributions (p>0.05,MannWhitneyUtest). Distributionswerenormalized to theirmax values. (B)Exonswith high ECIvalues tend tooccupya position close to thecenter

oftranscripts that include theexon of interest. In this figure, the corresponding position of an exon that is close to the5 end (left to thecenter, indicatedby 0) is displayed

by a negative value,whilean exonclose to the 3 endis indicatedby a positive value.

with a moderate or low authority score and low ECI values in the

graph.

3.3. Stochasticity makes a minor contribution to the variance ofECI values

The power-law distribution cannot result only from random

splicing of the RUNX1RUNX1T1 exons. Thus, cumulative dis-

tribution of the empirical ECI values is clearly and significantly

different from the theoretical curve for a random graph (Fig. 3A).

Nevertheless, we evaluated contribution of randomness to the

local combinatorics of the RUNX1RUNX1T1 exons because it is

an important source of diversity of alternative splicing events in

human transcriptome (Melamud and Moult, 2009; Pickrell et al.,

2010).

For this purpose, we first identified noise splicing events that

lead to the formation of non-coding transcripts or unproductive

transcripts with a PTC. These two categories of noise accountfor about 13% and 26% of the splicing events diversity in the

RUNX1RUNX1T1 transcripts, respectively. However, the empiri-

calcumulative distributionof ECIvaluesbecomes slightly different

only after correction forunproductivesplicing butnot after correc-

tion forsplicingeventsthat lead tonon-coding transcripts (Fig.3A).

Additionally, we evaluated the relationship between position

of an exon in transcripts and its ECI value. We performed this

analysis because the fusion gene is characterized by a large vari-

ety of cassette UTR exons and exons from the breakpoint region.

We expected that such organization of the gene gives a chance to

the nearest constitutive exons to get a high rank ECI. However,we

foundonly amoderatecorrelation between thepositionaldistribu-

tion of the exons and the distribution of their ECI values (=0.455,

p=2.2106

; Fig. 3B).Froma random forests-based nonlinearmultiple regression,we

found that the noise splicing and the positional chance explained

not more than 12% of the ECI variance. Therefore, stochasticity is

only a minor factor in formation of the ECI value.

3.4. Deregulation of the NMDgenes in leukemia cells may explain

a high abundance of unproductive RUNX1RUNX1T1 transcripts

Inourdataset,about38%ofmRNAmolecules arePTC-containing

transcripts. Although these transcripts are potential targets for

NMD system, their expression remains at relatively high level.

For example, inclusion of exon 15a as an internal exon (amplicon

exons 15a-15, Fig. 4A) always leads to formation of transcripts

with a PTC, which expression is comparable with that of some

transcripts without PTC (for instance, mRNAs with termination in

exon 17a; amplicon exons 16-17a, Fig. 4B).

Highfrequencyof transcripts containingPTC suggests thatthere

can be a dysfunction of the NMD system in t(8;21)-positive AML.To check this hypothesis, we developed a shortlist of the most

important NMD genes that are responsible for different steps of

decay of PTC-containing mRNA molecules. Real-time PCR con-

firmed differential expression of some of these genes in leukemia

cells comparing to the normal CD34+ HPSC, BMMNC and PBMNC

(Fig. 4C). In particular, we found a disbalanced expression of

some key components of the exon junction complexes (EJCs) in

leukemia blasts: CASC3 gene was 3.23.9-fold downregulated,

whereas MAGOH and RBM8A genes were from 1.6 to 3.3 times

upregulated.Herewith,it wasshownthat theMAGOH-RBM8Ahet-

erodimer through interaction withEJCs regulatorWIBG/PYMleads

to the disassembly of EJCs in the cytoplasm and enhances trans-

lation of EJCs-bearing spliced mRNAs by recruiting them to the

ribosomal48Spreinitiationcomplex (Gehring et al., 2005). Anotherobservationwas that theexpression of SMG1 andUPF2 genes, cod-

ing important components of the NMD machinery, is significantly

reduced (on average 1.64.4 times) and the expression of UPF1

gene, coding the key effector of the whole NMD process, tends to

decrease with statistical significance observed only in the com-

parison with BPMNC. There was also a 1.85.0-fold decrease in

the expression of UPF3A (comparing to BMMNC and PBMNC) and

GSPT1 (when compared to CD34+ HPSC and BMMNC) genes, cod-

ing proteins responsible for the recruitment of UPF1 to ribosomes

stalled on PTC-containing mRNAs. Similar decrease was found for

SMG5, SMG6 and SMG7 genes, coding downstream effectors that

are involved in degradation of transcriptsmarkedforNMD. Finally,

expression of DCP1B (comparing only to CD34+ HPSC) and DCP2

(comparing to all types of the normal hematopoietic cells) genes,coding core components of the mRNA decapping complex, was

diminished from 2.4 to 6.5 times.

In addition, we observed a significant correlation between the

expression ofNMDgenesand someof mRNAisoformsof the fusion

gene (Fig. 4D). Altogether, these results indicate that NMD genes

fromdifferent steps of decayof PTC-containingmRNAshave a spe-

cific expression profile in t(8;21)-positive AML that presumably

contributes to the diversity of RUNX1RUNX1T1 transcripts.

3.5. Different attractiveness of the RUNX1RUNX1T1 exons for

alternative splicing is associatedwith sequence-related features

The simplest explanation for the observed power-lawdistribu-

tion is a preferential attachment (Albert and Barabasi, 2002). In


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Fig. 4. Activity of the NMD system in children t(8;21)-positive AML cells is deregulated. (A) In NMD study, five different RUNX1RUNX1T1 cDNA-based ampliconswere

quantified by real-timePCR. Quantity of theamplicon exons15a-15 indicatesthe expression level of transcripts comprising exon 15a as an internal exon.When exon 15a

is used as an internal exon, it introduces a PTCin themature transcript. (B)According to real-timePCR andstatistical analysis,RUNX1RUNX1T1 mRNA isoforms containing

exons 11-12a, 15a, 15a-15, 16-17a or 16-17 are differentially expressed in leukemia cells. Herewith, expression level of transcripts with internal exon 15a is similar to

transcripts with exons 16-17a, which do not include a PTC (p=0.79, MannWhitney Utest). However, it is assumed that exon 15a can be not only an internal but also a

3UTRexon. Inparticular,the overall expression level of transcripts containingexon 15a is significantlyhigher thanlevel of thePTC-containingtranscriptswithexons 15a-15

(p


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Fig. 6. Sequence features of humanRUNX1RUNX1T1 exons andflanking introns may determine the value of the ECI. (A) All sequence features were extracted from three

classes of mRNA structure elements. The first class includes features of the target exon (exon of interest; STEE ) and its flanking 5 (STE

51) and 3 (STE

31) intronic sequences. The

second class contains features of the upstream first neighboring exons (SUSEE ) and their flanking 5 (SUSE

51) and 3 (SUSE

31 ) intronic sequences. Finally, the third class includes

features of the downstream first neighboring exons (SDSEE ) and corresponding flanking 5 (SDSE

51) and 3 (SDSE

31) intronic sequences. (B) Sequence features are not equal in

importance for the prediction of the ECI value. The important features were ranked according to the mean decrease in the accuracy of the ECI value prediction after the

random permutation of theoriginal feature values. An insertion of Venn diagram shows an overlap between the selected important features for the three types of the ECI.

(C) A complex relationshipbetween thesequence features andthe ECIvalue. None of thesequence features can reliably predict theECI value. Such predictions canbe made

on a compendium of features.The inner track of Circosplot includes sectors of combined set of features that were selected as significant in prediction of thevalueof thein-,

out- and/ortotal-ECI.Widthof each sectoris proportionalto thestrengthof thecorrespondingfeature effecton theECI value.Positive ornegativecharacter of this effectwas

inferred from thecorrelation analysis. Theouter track of theplot contains features of differentsubclasses.(D) Ourcompendium of thesequence features permits to predict

the values of the ECI by regression random forests with a high accuracy. The line plot demonstrates a binned distribution of Spearmans between the real values of the

ECI from the test subset of empirical data and thepredicted values. This plot is based on 1000 simulations of theoriginal and randomly permutated ECI values. Lines 1 and

1 represent theoriginal andpermutated total-ECI, lines 2 and2 show the original and permutated in-ECI, and lines 3 and 3 indicate the original and permutatedout-ECI,

respectively.

Model experiments demonstrated that selected features per-

mit to predict the ECI value with high accuracy. For instance, the

median of Spearmans between values predicted by the trained

algorithm and empirical values of the total-ECI is 0.86 (Fig. 6D),

and the adjusted coefficient of determination equals to 0.75. We

observed thesame results forin- andout-ECIs (Fig. 6D). Altogether,

our data provide an evidence that sequence features and the ECI

value of theRUNX1RUNX1T1 exons are closely interrelated.

3.6. Differential expression of splicing genes correlates withabundance of the RUNX1RUNX1T1 isoforms

It iswell known that cis-regulatorymotifs serve as guidemarks

for splicing trans-factors (Wang et al., 2012). Therefore, theoreti-

cally discovered interconnection between a sequence feature and

an exon splicingmay be only a statistical phenomenon if cells lack

the corresponding trans-acting protein. To verify some theoretical

achievements from the above mentioned data mining, we devel-

oped a short list of themost importantsplicinggenesandevaluated

their expression by real-timePCR.

The most interesting observation was related to the expres-

sion of the RBFOX3 gene. This gene is not expressed or expressed

under the threshold of the real-time PCR sensitivity in normal

hematopoietic cells. However, both qualitative and quantitative

analysesconfirmeditsexpressionin t(8;21)-positive leukemiacells

(Fig. 7A and B). Moreover, we found RBFOX3binding sites in flank-

ing introns of some RUNX1RUNX1T1 exons. Frequency of these

sites was selected as an important feature by the regression ran-

domforestsalgorithm(SupplementaryTable 7), andweobserveda

significant correlationbetween expression of theRBFOX3geneand

expression of somemRNA isoforms of the fusion gene in leukemia

cells (Fig. 7D).

The differential expression and the significant correlationwere

confirmed for other splicing genes aswell, in particular, for SRSF6,

RBM25, PTBP1 and TIA1genes (Fig. 7C andD). Therefore, a numberof splicing-related genes are differentially expressed in t(8;21)-

positive leukemia cells. This fact may contribute to the diversity

of mRNAproducts of the fusion gene.

3.7. Exons with high ECI values are hot points of the

RUNX1RUNX1T1 mRNA splicing

Apower-lawgraph ishighlysensitiveto targetedattacksagainst

important vertices (Iyer et al., 2013; Schneidera et al., 2011). The

RUNX1RUNX1T1 exon graph has a power-law component and it

mayhave thesameproperty.To check this hypothesis,wemodeled

a skipping of exons by the splicing system and an outcome of such

a skipwas evaluated with five metrics (Fig. 8).


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Fig. 7. Genes of splicing factors differentially expressed in t(8;21)-positive AMLblasts. (A) TheRBFOX3gene is not expressedor expressedunderthe thresholdof detection

by RT-PCR in normalhematopoietic cells butthis gene is expressed in leukemia cells.The lanes on theupperelectrophoregram:Fermentas GeneRulerTM 100bp DNALadder

Plus (1), amplification of cDNA of the TBP gene from Kasumi-1 cells (2), amplification of cDNA of the RBFOX3 gene fromnormal PBMNC (3, 5), BMMNC (7, 9), CD34+ HPSC

(11, 13) and from Kasumi-1 cells (15) and amplificationof cDNA of theRBFOX3gene from respective RT negative controls (4, 6, 8, 10, 12, 14, 16). The lanes on the bottom

electrophoregram: Fermentas GeneRulerTM 100bp DNA Ladder Plus (1), amplification of cDNA of the RBFOX3 gene from the bonemarrow samples of nine children with

t(8;21)-positive AML(2, 4,6, 8,10, 12, 14, 16, 18) and respectiveRT negative controls (3, 5, 7, 9, 11, 13, 15, 17, 19). (B) Real-timePCRconfirms thedifferential expression

of the RBFOX3 gene in normal and malignant hematopoietic cells. Expression of the RBFOX3 gene was normalized relative to the expression of the TBP gene, and then

re-normalized to theexpressionof this gene in Kasumi-1cells. Thepicture showsanaveragedexpression of theRBFOX3 gene in4 samples of normalCD34+HPSC, 5 samples

ofnormal BMMNC,5 samples of normal PBMNC and9 bonemarrowsamples of childrenwith t(8;21)-positive AML. (C) There is a significant (according toMannWhitneyU

test) differential expressionof thesplicing factors genes in leukemia cellsin comparison withnormal hematopoietic cells. (D) Correlation between expressionof the splicing

factors genes andmRNA isoforms of theRUNX1RUNX1T1 gene.

We found that targeted skipping of exons with the top ranked

ECI values leads to a very rapid drop in values of all fivemetrics. At

thesame time,weobserved a rather slow decline ofmetrics values

when low ECI exons were skipped, and that was proportional to

the fraction of excluded exons. Herewith, the above observations

were applied to both experimentally detected and theoretically

possible transcripts that canbe generatedby exon graph. Interest-

ingly,a setof expectedtranscripts includes43,486entitiesof which

experimentallyverified transcripts representonly0.3%. Altogether,

these results indicate that the power-lawcomponent of the fusion

gene organization confers a high flexibility to alternative splicing

of RUNX1RUNX1T1 transcripts.

4. Discussion

In this work, we showed that local combinatorics of the

RUNX1RUNX1T1 exons followa power-lawbehavior.This behav-

ior is also typical for exons of normal RUNX1 and RUNX1T1 genes

and for the whole set of exons of human transcriptome (data not

shown).

The observed power-law distribution has four key properties.

First, the vast majority of exons has low values of the ECI. These

exons aremostly represented by constitutive exons encoding con-

served RUNX1T1 domains of the fusion protein, UTR exons and

cassette exons from the breakpoint region. In fact, theNLS-NHR2-

NHR3-NHR4 coding part of the fusion gene (from 3

-end of exon

11 to 5-end of exon 17) is the most constant in terms of splicing.

This is consistent with the empirical data showing that RUNX1T1

domains arevery importantfor thefusionprotein functionandany

alternative splicingeventsin this areamay cardinally change activ-

ity oftheprotein (Parket al.,2009;Sunetal.,2013;Yanetal., 2006).

At the same time, a high diversity and a low individual abundance

in transcripts may be the main reasons why UTR exons and exons

from the breakpoint region are fallen in a group with low values of

the ECI.

Second, a small part of exons have high ECI values. This part

includes constitutive exons-hubs that participate in different

splicing mechanismsandmostly contribute to alternative splicing.Thus, about 80% of the splicing events with exon 5 use alternative

5 splice sites of 5UTR exons. At the same time, about 70% of the

splicing events involving exons 6 and 8b use alternative 5 or 3

splice sites of cassette exons from the breakpoint region. Most of

these exons were found by LaFiura et al. (2008), we identifiedonly

twonewsequencesfromthebreakpoint region. Perhaps, this isdue

to patient specificity and rarity of such exons.

It is interesting tonote thatmostof the exonswithhighECI val-

uesbelong tothesecondand thethirdclustersbasedon Kleinbergs

authority score. Moreover, these exons encode Runt homology

domain RHD, NHR1 domain and the polypeptide bridge, uniting

RUNX1- and RUNX1T1-parts of the fusion protein. Herewith, the

RHD domain is responsible for specific DNA binding and the NHR1

domain provides heterodimerization of the fusion protein with


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0

20

40

60

80 1000.0

0.2

0.4

0.6

0.8

1.0

Normalized

valueofmetric

Fraction of skipped exons0 20 40 60 80 100

0.0

0.2

0.4

0.6

0.8

1.0

Normalized

valueofmetric

Fraction of skipped exons

0 20 40 60 80 1000.0

0.2

0.4

0.6

0.8

1.0

Normalizedvalueofmetric

Fraction of skipped exons0 20 40 60 80 100

0.0

0.2

0.4

0.6

0.8

1.0

Normalizedvalueofmetric

Fraction of skipped exons

A B

Legend:diversity of transcriptsaverage size (in number of exons) of transcriptsaverage length (in number of nucleotides) of transcriptsaverage length of ORFportion of transcripts containing PTC

Fig. 8. In silicomodeling supports a strongsensitivity of splicing of RUNX1RUNX1T1 transcripts to skipping of exonswith high ECIvalues. (A) Skipping of exons that were

listedin thedescending order of their ECI values: experimentally verified transcripts (onthe top), predictedtranscripts(on thebottom).(B) This picture is similar to (A), but

exonswere excluded from splicing process in theascending order of values of their ECI.

othertranscriptional regulators (HugandLazar,2004;Tahirovetal.,

2001; Zhang et al., 2004). Consequently, intense alternative splic-

ing of these exons can effect DNA-binding activity of the fusion

protein(s), ways of RUNX1- and RUNX1T1-parts combination and

ability to formmultimeric regulatory complexes.

Third,the ratiobetween thenumberofexonswith lowand high

values of theECI is constant because thepower-lawdistribution is

scale-free (Newman, 2005). In fact, this ratio did not change and

exonsdidnot alter rankswhenweexpandedtheRUNX1RUNX1T1

exongraphby ESTs( =0.99,p


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decoding of exon splicing patterns in the human runx1–runx1t1

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