The spliceosome is a huge molecular machine that assembles dynamically onto its pre-mRNA substrates. A new study based on interactome analysis provides clues about how splicing-regulatory proteins modulate assembly of the spliceosome to either activate or repress splicing.National Cancer Institute (U.S.

Burge, Christopher B.

Dominguez, Daniel

Genome Biology

PubMed

Daniel Dominguez

Christopher B. Burge

Crossref

Dominguez and Burge Genome Biology  (2015) 16:135 DOI 10.1186/s13059-015-0707-0RESEARCH HIGHLIGHT Open AccessInteractome analysis brings splicing into focusDaniel Dominguez1 and Christopher B. Burge1,2*Please see related Research article: http://www.genomebiology.com/2015/16/1/119/abstractAbstractThe spliceosome is a huge molecular machine thatassembles dynamically onto its pre-mRNA substrates.A new study based on interactome analysis providesclues about how splicing-regulatory proteins modulateassembly of the spliceosome to either activate orrepress splicing.Modeling the splicing interactomeIntroductionMassive molecular machines comprising dozens tohundreds of proteins, as well as RNA components,catalyze transcription, RNA splicing and translation.These machines vary substantially in their modes ofassembly and regulation. While ribosomes come preas-sembled (in prokaryotes) or in just two pieces (eukary-otes), the spliceosome that catalyzes removal of intronsfrom eukaryotic primary transcripts comprises severalpieces that must assemble de novo on each of its in-tronic substrates. Once assembled, the spliceosome isone of the largest complexes in the cell, comprising hun-dreds of proteins linked by uncounted stable interac-tions, as well as transient interactions that form asrearrangements occur during splicing [1]. These piecesinclude five small nuclear ribonucleoprotein particles(snRNPs), each formed from a distinct small nuclearRNA (snRNA) wrapped in a shell of proteins. ThesesnRNPs, as well as other proteins, comprise the catalyticcore of the spliceosome.Dozens of protein factors can regulate splicing, me-diating alternative splicing outcomes, such as exonskipping, inclusion or intron retention, to producedistinct mRNA and protein isoforms from a singlegene locus [1, 2]. These factors can be roughlygrouped into splicing activators, splicing repressors* Correspondence: cburge@mit.edu1Department of Biology, Massachusetts Institute of Technology, Cambridge,MA 02142, USA2Department of Biological Engineering, Massachusetts Institute ofTechnology, Cambridge, MA 02142, USA© 2015 Dominguez and Burge. This is an OpeAttribution License (http://creativecommons.oreproduction in any medium, provided the orDedication waiver (http://creativecommons.orunless otherwise stated.and factors with more-variable activities, but themechanisms by which activation and repression occurare still not well understood. Akerman and colleagues[3] report the generation of a spliceosome-centricinteractome to identify specific features of splicingactivators and repressors that might help uncoverregulatory mechanisms.The major spliceosome, which is responsible for splicingthe vast majority of introns, contains approximately 150to 300 proteins, depending on its stage of assembly andother variables. Some of these proteins form highlystable complexes, such as the rings formed by Smproteins, whereas others interact only transiently at spe-cific stages of spliceosome assembly, making the splicinginteractome particularly complex.In an effort to make sense of this complexity and tounderstand the functions of splicing regulators, Akermanand colleagues [3] build a probabilistic model of the pro-tein–protein interactions (PPIs) among splicing factorscalled the ‘probabilistic spliceosome’ (or PS-network)(Fig. 1). They use the Human Protein Reference Database(HPRD) as a source of data on interactions, which is basedmostly on large-scale yeast two-hybrid (Y2H) experi-ments. Central to their analysis is the graph theory con-cept of ‘transitivity’, or clustering coefficient, whichmeasures the extent to which a pair of nodes in a networkshare interactions with other nodes. For example, Y2Hmight have failed to detect an interaction between twosplicing factors ‘A’ and ‘B’ but successfully detected thateach interacts with several of the same spliceosomal pro-teins, yielding a high transitivity score that enables the in-ference that A and B are often in proximity and likelyinteract. The authors also use gene-expression data, fol-lowing the logic that interacting proteins should tend tobe coexpressed. The Y2H and expression data were com-bined in a Bayesian approach to produce a composite‘probability of interaction’ (Pin) for each pair of proteins,and PS-networks were built from interactions pre-dicted at each of several Pin cutoffs. These networksn Access article distributed under the terms of the Creative Commonsrg/licenses/by/4.0), which permits unrestricted use, distribution, andiginal work is properly credited. The Creative Commons Public Domaing/publicdomain/zero/1.0/) applies to the data made available in this article,SRSF1AS factors U2, SF3 A ComplexEJC U1 U4/U6°U5 HNRNPA1HNRNPs RBMs Fig. 1 Representation of the interactomes of a prototypical splicing activator, SRSF1, and a prototypical splicing repressor, HNRNPA1. Akermanand colleagues [3] report that activators — which promote exon inclusion (shown at right) — form more interactions with components of thespliceosome than repressors, which promote exon exclusion (left). EJC exon junction complex; hnRNP heterogeneous nuclear ribonucleoprotein;RBM RNA binding motif; SRSF serine/arginine-rich splicing factorDominguez and Burge Genome Biology  (2015) 16:135 Page 2 of 4were then tested on an independently generated Y2Hinteraction matrix screen of splicing factors that de-tected over 600 interactions between approximately200 proteins [4]. The results were promising, detecting55 % of spliceosome interactions at a moderate thresh-old (Pin ≥ 0.1) that retained a high prediction specificityof 85 %.The resulting PS-network identifies approximately tenclusters of proteins based on similarity in interactionprofiles, using an approach similar to that of Ravasz andcolleagues [5]. Many of these clusters correspond toestablished functional groupings of factors, with a clusterof U1/U2 snRNP factors, a cluster associated withthe Bact and C spliceosomal complexes, and a clusterthat included SR proteins and heterogeneous nuclearribonucleoproteins (hnRNPs). These clusters couldprovide insights into the predominant stage of spli-cing at which a particular factor acts. One could alsocompare these data with those of clusters of splicingfactors identified by Papasaikas et al. [6], who usedan orthogonal, functional approach involving cluster-ing based on similarity in the pattern of splicingchanges observed following depletion of spliceosomaland splicing regulatory factors through RNA interferenceDominguez and Burge Genome Biology  (2015) 16:135 Page 3 of 4(RNAi). The apparent similarities between these cluster-ings support the intuitive notion that interacting proteinstend to function similarly in splicing regulation.Implications for the regulation of splicingWhether an intron is spliced ultimately comes down towhether a catalytically active spliceosome is assembledon it or not. Regulation of splicing can involve eitherrecruiting components of the splicing machinery toparticular locations or blocking their access, or it canoccur by modulating interactions between core splicingcomponents, such as snRNPs. The Akerman studyfocuses on two canonical classes of splicing factors — SRproteins and hnRNPs — both of which are widelyexpressed and well conserved across metazoans. Most SRproteins (serine/arginine-rich splicing factors SRSF1–SRSF7) can activate inclusion of exons to which they bindat sites known as exonic splicing enhancers, whereasSRSF9–SRSF11 mostly act as splicing repressors. ThehnRNPs also include several factors that act mostly as ac-tivators, and others such as hnRNPs A1 and A2B1 thatfunction primarily as repressors, based on a recent com-prehensive genomic analysis of binding and regulation [7].A number of studies have explored the mechanisms bywhich activators and repressors function, supporting avariety of models. These include models in which activa-tors recruit snRNPs to nearby sites or promote exon-bridging interactions between snRNPs, and models inwhich repressors bind on both sides of and ‘loop out’exons or inhibit interactions between snRNPs [8]. How-ever, the detailed mechanisms involved in the regulationof splicing remain poorly understood.Akerman and colleagues observe that splicing activatorproteins and splicing repressors exhibit very differentpatterns of interactions in the PS-network. Activatorssuch as SRSF1 have high predicted connectivity to mul-tiple subsets of spliceosomal factors, whereas repressorssuch as HNRNPA1 interact with fewer factors. Forexample, SRSF1 was predicted to interact with compo-nents of all five snRNPs, including with factors of thecore Sf3 family of U2 snRNP, whereas HNRNPA1 waspredicted to interact mostly with other hnRNP proteins.Furthermore, the SRSF1 interactome included manymore direct and RNA-independent PPIs, whereas thePPIs of HNRNPA1 were more often RNA dependent.The authors followed up these observations byperforming immunoprecipitation-mass-spectrometry,confirming many of the interactions identified bythe PS-network, including a novel interaction betweenSRSF1 and specific Sf3a subunits. Direct interactionsbetween SRSF1 and the Sf3a complex support a recruit-ment model, whereby SR proteins facilitate the assemblyof the U2 snRNP (or stabilize its binding) on the pre-mRNA to which they are bound. Previous biochemicalexperiments have shown that SRSF1 is present in U2-containing cellular fractions and stabilizes the U2 snRNPon pre-mRNA in in vitro splicing assays [9].A steric-hindrance model for hnRNP-mediated repres-sion only requires that hnRNPs block the accessibility ofcore splicing machinery to pre-mRNA. While hnRNPsare not particularly bulky proteins, their known abilityto bind cooperatively and spread on or loop out pre-mRNA molecules could help to explain how hnRNPbinding to relatively short sequence motifs with moder-ate affinity can result in blockade of spliceosome assem-bly [10]. Akerman and colleagues also identify directinteractions between splicing activators and repressors(e.g., SRSF1 with SRSF9 and SRSF10). Such interactionsmight help to explain why canonical activators cansometimes repress, and canonical repressors can some-times activate.ConclusionsAn important question for future study is to characterizethe interactomes and understand regulation by thedozens of other splicing regulators that are not SR pro-teins or hnRNPs. These factors can often activate or re-press splicing with similar frequency, usually in amanner that depends on where they bind in relation tothe regulated exons, often described by an ‘RNA map’.For example, several factors might activate exon inclu-sion when bound downstream of the exon, but might re-press splicing when bound upstream.Ultimately, one hopes that our understanding of thesplicing interactome will be unified with our understand-ing of the structures and regulatory functions of individualfactors and complexes in order to fully illuminate theunderlying mechanisms.AbbreviationshnRNP: Heterogeneous nuclear ribonucleoprotein; HPRD: Human ProteinReference Database; Pin: Probability of interaction; PPI: Protein–proteininteraction; RNAi: RNA interference; snRNA: Small nuclear RNA; snRNP: Smallnuclear ribonucleoprotein particle; SRSF: Serine/arginine-rich splicing factor;Y2H: Yeast two-hybrid.Competing interestsThe authors declare that they have no competing interests.Authors’ contributionsDD and CBB jointly wrote this article and have approved the final manuscript.AcknowledgementsThe authors’ research is supported by grants from the NIH (CBB).References1. Wahl MC, Will CL, Lührmann R. The spliceosome: design principles of adynamic RNP machine. Cell. 2009;136:701–18. doi:10.1016/j.cell.2009.02.009.2. Lee Y, Rio DC. Mechanisms and regulation of alternative pre-mRNAsplicing. Annu Rev Biochem. 2015;84:291–323. doi:10.1146/annurev-biochem-060614-034316.Dominguez and Burge Genome Biology  (2015) 16:135 Page 4 of 43. Akerman M, Fregoso OI, Das S, Ruse C, Jensen MA, Pappin DJ, et al.Differential connectivity of splicing activators and repressors to the humanspliceosome. Genome Biol. 2015;16:119. doi:10.1186/s13059-015-0682-5.4. Hegele A, Kamburov A, Grossmann A, Sourlis C, Wowro S, Weimann M, et al.Dynamic protein-protein interaction wiring of the human spliceosome. MolCell. 2012;45:567–80. doi:10.1016/j.molcel.2011.12.034.5. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási AL. Hierarchicalorganization of modularity in metabolic networks. Science. 2002;297:1551–5.6. Papasaikas P, Tejedor JR, Vigevani L, Valcárcel J. Functional splicing networkreveals extensive regulatory potential of the core spliceosomal machinery.Mol Cell. 2015;57:7–22. doi:10.1016/j.molcel.2014.10.030.7. Huelga SC, Vu AQ, Arnold JD, Liang TY, Liu PP, Yan BY, et al. Integrativegenome-wide analysis reveals cooperative regulation of alternative splicingby hnRNP proteins. Cell Rep. 2012;1:167–78.8. Chen M, Manley JL. Mechanisms of alternative splicing regulation: insightsfrom molecular and genomics approaches. Nat Rev Mol Cell Biol.2009;10:741–54. doi:10.1038/nrm2777.9. Tarn WY, Steitz JA. Modulation of 5′ splice site choice in pre-messenger RNAby two distinct steps. Proc Natl Acad Sci U S A. 1995;92:2504–8.10. Okunola HL, Krainer AR. Cooperative-binding and splicing-repressive propertiesof hnRNP A1. Mol Cell Biol. 2009;29:5620–31. doi:10.1128/MCB.01678-08.

Interactome analysis brings splicing into focus

Burge, Christopher B

DSpace@MIT

Springer - Publisher Connector

Dominguez and Burge Genome Biology  (2015) 16:135 
DOI 10.1186/s13059-015-0707-0RESEARCH HIGHLIGHT Open AccessInteractome analysis brings splicing into focus
Daniel Dominguez1 and Christopher B. Burge1,2*
Please see related Research article: http://www.genomebiology.com/2015/16/1/119/abstractAbstract
The spliceosome is a huge molecular machine that
assembles dynamically onto its pre-mRNA substrates.
A new study based on interactome analysis provides
clues about how splicing-regulatory proteins modulate
assembly of the spliceosome to either activate or
repress splicing.Modeling the splicing interactomeIntroduction
Massive molecular machines comprising dozens to
hundreds of proteins, as well as RNA components,
catalyze transcription, RNA splicing and translation.
These machines vary substantially in their modes of
assembly and regulation. While ribosomes come preas-
sembled (in prokaryotes) or in just two pieces (eukary-
otes), the spliceosome that catalyzes removal of introns
from eukaryotic primary transcripts comprises several
pieces that must assemble de novo on each of its in-
tronic substrates. Once assembled, the spliceosome is
one of the largest complexes in the cell, comprising hun-
dreds of proteins linked by uncounted stable interac-
tions, as well as transient interactions that form as
rearrangements occur during splicing [1]. These pieces
include five small nuclear ribonucleoprotein particles
(snRNPs), each formed from a distinct small nuclear
RNA (snRNA) wrapped in a shell of proteins. These
snRNPs, as well as other proteins, comprise the catalytic
core of the spliceosome.
Dozens of protein factors can regulate splicing, me-
diating alternative splicing outcomes, such as exon
skipping, inclusion or intron retention, to produce
distinct mRNA and protein isoforms from a single
gene locus [1, 2]. These factors can be roughly
grouped into splicing activators, splicing repressors* Correspondence: cburge@mit.edu
1Department of Biology, Massachusetts Institute of Technology, Cambridge,
MA 02142, USA
2Department of Biological Engineering, Massachusetts Institute of
Technology, Cambridge, MA 02142, USA
© 2015 Dominguez and Burge. This is an Ope
Attribution License (http://creativecommons.o
reproduction in any medium, provided the or
Dedication waiver (http://creativecommons.or
unless otherwise stated.and factors with more-variable activities, but the
mechanisms by which activation and repression occur
are still not well understood. Akerman and colleagues
[3] report the generation of a spliceosome-centric
interactome to identify specific features of splicing
activators and repressors that might help uncover
regulatory mechanisms.The major spliceosome, which is responsible for splicing
the vast majority of introns, contains approximately 150
to 300 proteins, depending on its stage of assembly and
other variables. Some of these proteins form highly
stable complexes, such as the rings formed by Sm
proteins, whereas others interact only transiently at spe-
cific stages of spliceosome assembly, making the splicing
interactome particularly complex.
In an effort to make sense of this complexity and to
understand the functions of splicing regulators, Akerman
and colleagues [3] build a probabilistic model of the pro-
tein–protein interactions (PPIs) among splicing factors
called the ‘probabilistic spliceosome’ (or PS-network)
(Fig. 1). They use the Human Protein Reference Database
(HPRD) as a source of data on interactions, which is based
mostly on large-scale yeast two-hybrid (Y2H) experi-
ments. Central to their analysis is the graph theory con-
cept of ‘transitivity’, or clustering coefficient, which
measures the extent to which a pair of nodes in a network
share interactions with other nodes. For example, Y2H
might have failed to detect an interaction between two
splicing factors ‘A’ and ‘B’ but successfully detected that
each interacts with several of the same spliceosomal pro-
teins, yielding a high transitivity score that enables the in-
ference that A and B are often in proximity and likely
interact. The authors also use gene-expression data, fol-
lowing the logic that interacting proteins should tend to
be coexpressed. The Y2H and expression data were com-
bined in a Bayesian approach to produce a composite
‘probability of interaction’ (Pin) for each pair of proteins,
and PS-networks were built from interactions pre-
dicted at each of several Pin cutoffs. These networksn Access article distributed under the terms of the Creative Commons
rg/licenses/by/4.0), which permits unrestricted use, distribution, and
iginal work is properly credited. The Creative Commons Public Domain
g/publicdomain/zero/1.0/) applies to the data made available in this article,
SRSF1
AS factors 
U2, SF3 
A Complex
EJC 
U1 
U4/U6°U5 
HNRNPA1
HNRNPs 
RBMs 
Fig. 1 Representation of the interactomes of a prototypical splicing activator, SRSF1, and a prototypical splicing repressor, HNRNPA1. Akerman
and colleagues [3] report that activators — which promote exon inclusion (shown at right) — form more interactions with components of the
spliceosome than repressors, which promote exon exclusion (left). EJC exon junction complex; hnRNP heterogeneous nuclear ribonucleoprotein;
RBM RNA binding motif; SRSF serine/arginine-rich splicing factor
Dominguez and Burge Genome Biology  (2015) 16:135 Page 2 of 4were then tested on an independently generated Y2H
interaction matrix screen of splicing factors that de-
tected over 600 interactions between approximately
200 proteins [4]. The results were promising, detecting
55 % of spliceosome interactions at a moderate thresh-
old (Pin ≥ 0.1) that retained a high prediction specificity
of 85 %.
The resulting PS-network identifies approximately ten
clusters of proteins based on similarity in interaction
profiles, using an approach similar to that of Ravasz and
colleagues [5]. Many of these clusters correspond to
established functional groupings of factors, with a clusterof U1/U2 snRNP factors, a cluster associated with
the Bact and C spliceosomal complexes, and a cluster
that included SR proteins and heterogeneous nuclear
ribonucleoproteins (hnRNPs). These clusters could
provide insights into the predominant stage of spli-
cing at which a particular factor acts. One could also
compare these data with those of clusters of splicing
factors identified by Papasaikas et al. [6], who used
an orthogonal, functional approach involving cluster-
ing based on similarity in the pattern of splicing
changes observed following depletion of spliceosomal
and splicing regulatory factors through RNA interference
Dominguez and Burge Genome Biology  (2015) 16:135 Page 3 of 4(RNAi). The apparent similarities between these cluster-
ings support the intuitive notion that interacting proteins
tend to function similarly in splicing regulation.
Implications for the regulation of splicing
Whether an intron is spliced ultimately comes down to
whether a catalytically active spliceosome is assembled
on it or not. Regulation of splicing can involve either
recruiting components of the splicing machinery to
particular locations or blocking their access, or it can
occur by modulating interactions between core splicing
components, such as snRNPs. The Akerman study
focuses on two canonical classes of splicing factors — SR
proteins and hnRNPs — both of which are widely
expressed and well conserved across metazoans. Most SR
proteins (serine/arginine-rich splicing factors SRSF1–
SRSF7) can activate inclusion of exons to which they bind
at sites known as exonic splicing enhancers, whereas
SRSF9–SRSF11 mostly act as splicing repressors. The
hnRNPs also include several factors that act mostly as ac-
tivators, and others such as hnRNPs A1 and A2B1 that
function primarily as repressors, based on a recent com-
prehensive genomic analysis of binding and regulation [7].
A number of studies have explored the mechanisms by
which activators and repressors function, supporting a
variety of models. These include models in which activa-
tors recruit snRNPs to nearby sites or promote exon-
bridging interactions between snRNPs, and models in
which repressors bind on both sides of and ‘loop out’
exons or inhibit interactions between snRNPs [8]. How-
ever, the detailed mechanisms involved in the regulation
of splicing remain poorly understood.
Akerman and colleagues observe that splicing activator
proteins and splicing repressors exhibit very different
patterns of interactions in the PS-network. Activators
such as SRSF1 have high predicted connectivity to mul-
tiple subsets of spliceosomal factors, whereas repressors
such as HNRNPA1 interact with fewer factors. For
example, SRSF1 was predicted to interact with compo-
nents of all five snRNPs, including with factors of the
core Sf3 family of U2 snRNP, whereas HNRNPA1 was
predicted to interact mostly with other hnRNP proteins.
Furthermore, the SRSF1 interactome included many
more direct and RNA-independent PPIs, whereas the
PPIs of HNRNPA1 were more often RNA dependent.
The authors followed up these observations by
performing immunoprecipitation-mass-spectrometry,
confirming many of the interactions identified by
the PS-network, including a novel interaction between
SRSF1 and specific Sf3a subunits. Direct interactions
between SRSF1 and the Sf3a complex support a recruit-
ment model, whereby SR proteins facilitate the assembly
of the U2 snRNP (or stabilize its binding) on the pre-
mRNA to which they are bound. Previous biochemicalexperiments have shown that SRSF1 is present in U2-
containing cellular fractions and stabilizes the U2 snRNP
on pre-mRNA in in vitro splicing assays [9].
A steric-hindrance model for hnRNP-mediated repres-
sion only requires that hnRNPs block the accessibility of
core splicing machinery to pre-mRNA. While hnRNPs
are not particularly bulky proteins, their known ability
to bind cooperatively and spread on or loop out pre-
mRNA molecules could help to explain how hnRNP
binding to relatively short sequence motifs with moder-
ate affinity can result in blockade of spliceosome assem-
bly [10]. Akerman and colleagues also identify direct
interactions between splicing activators and repressors
(e.g., SRSF1 with SRSF9 and SRSF10). Such interactions
might help to explain why canonical activators can
sometimes repress, and canonical repressors can some-
times activate.
Conclusions
An important question for future study is to characterize
the interactomes and understand regulation by the
dozens of other splicing regulators that are not SR pro-
teins or hnRNPs. These factors can often activate or re-
press splicing with similar frequency, usually in a
manner that depends on where they bind in relation to
the regulated exons, often described by an ‘RNA map’.
For example, several factors might activate exon inclu-
sion when bound downstream of the exon, but might re-
press splicing when bound upstream.
Ultimately, one hopes that our understanding of the
splicing interactome will be unified with our understand-
ing of the structures and regulatory functions of individual
factors and complexes in order to fully illuminate the
underlying mechanisms.
Abbreviations
hnRNP: Heterogeneous nuclear ribonucleoprotein; HPRD: Human Protein
Reference Database; Pin: Probability of interaction; PPI: Protein–protein
interaction; RNAi: RNA interference; snRNA: Small nuclear RNA; snRNP: Small
nuclear ribonucleoprotein particle; SRSF: Serine/arginine-rich splicing factor;
Y2H: Yeast two-hybrid.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
DD and CBB jointly wrote this article and have approved the final manuscript.
Acknowledgements
The authors’ research is supported by grants from the NIH (CBB).References
1. Wahl MC, Will CL, Lührmann R. The spliceosome: design principles of a
dynamic RNP machine. Cell. 2009;136:701–18. doi:10.1016/j.cell.2009.02.009.
2. Lee Y, Rio DC. Mechanisms and regulation of alternative pre-mRNA
splicing. Annu Rev Biochem. 2015;84:291–323. doi:10.1146/annurev-
biochem-060614-034316.
Dominguez and Burge Genome Biology  (2015) 16:135 Page 4 of 43. Akerman M, Fregoso OI, Das S, Ruse C, Jensen MA, Pappin DJ, et al.
Differential connectivity of splicing activators and repressors to the human
spliceosome. Genome Biol. 2015;16:119. doi:10.1186/s13059-015-0682-5.
4. Hegele A, Kamburov A, Grossmann A, Sourlis C, Wowro S, Weimann M, et al.
Dynamic protein-protein interaction wiring of the human spliceosome. Mol
Cell. 2012;45:567–80. doi:10.1016/j.molcel.2011.12.034.
5. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási AL. Hierarchical
organization of modularity in metabolic networks. Science. 2002;297:1551–5.
6. Papasaikas P, Tejedor JR, Vigevani L, Valcárcel J. Functional splicing network
reveals extensive regulatory potential of the core spliceosomal machinery.
Mol Cell. 2015;57:7–22. doi:10.1016/j.molcel.2014.10.030.
7. Huelga SC, Vu AQ, Arnold JD, Liang TY, Liu PP, Yan BY, et al. Integrative
genome-wide analysis reveals cooperative regulation of alternative splicing
by hnRNP proteins. Cell Rep. 2012;1:167–78.
8. Chen M, Manley JL. Mechanisms of alternative splicing regulation: insights
from molecular and genomics approaches. Nat Rev Mol Cell Biol.
2009;10:741–54. doi:10.1038/nrm2777.
9. Tarn WY, Steitz JA. Modulation of 5′ splice site choice in pre-messenger RNA
by two distinct steps. Proc Natl Acad Sci U S A. 1995;92:2504–8.
10. Okunola HL, Krainer AR. Cooperative-binding and splicing-repressive properties
of hnRNP A1. Mol Cell Biol. 2009;29:5620–31. doi:10.1128/MCB.01678-08.


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Interactome analysis brings splicing into focus

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