The effects of alternative splicing on transmembrane proteins in the mouse genome.
ABSTRACT Alternative splicing is a major source of variety in mammalian mRNAs, yet many questions remain on its downstream effects on protein function. To this end, we assessed the impact of gene structure and splice variation on signal peptide and transmembrane regions in proteins. Transmembrane proteins perform several key functions in cell signaling and transport, with their function tied closely to their transmembrane architecture. Signal peptides and transmembrane regions both provide key information on protein localization. Thus, any modification to such regions will likely alter protein destination and function. We applied TMHMM and SignalP to a nonredundant set of proteins, and assessed the effects of gene structure and alternative splicing on predicted transmembrane and signal peptide regions. These regions were altered by alternative splicing in roughly half of the cases studied. Transmembrane regions are divided by introns slightly less often than expected given gene structure and transmembrane region size. However, the transmembrane regions in single-pass transmembranes are divided substantially less often than expected. This suggests that intron placement might be subject to some evolutionary pressure to preserve function in these signaling proteins. The data described in this paper is available online at http://www.affymetrix.com/community/publications/affymetrix/tmsplice/.
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ABSTRACT: • Premise of the study: High-throughput sequencing of cDNA libraries prepared from diverse samples (RNA-seq) can reveal genome-wide changes in alternative splicing. Using RNA-seq data to assess splicing at the level of individual genes requires the ability to visualize read alignments alongside genomic annotations. To meet this need, we added RNA-seq visualization capability to Integrated Genome Browser (IGB), a free desktop genome visualization tool. To illustrate this capability, we present an in-depth analysis of abiotic stresses and their effects on alternative splicing of SR45a (AT1G07350), a putative splicing regulator from Arabidopsis thaliana. • Methods: cDNA libraries prepared from Arabidopsis plants that were subjected to heat and dehydration stresses were sequenced on an Illumina GAIIx sequencer, yielding more than 511 million high-quality 75-base, single-end sequence reads. Reads were aligned onto the reference genome and visualized in IGB. • Key results: Using IGB, we confirmed exon-skipping alternative splicing in SR45a. Exon-skipped variant AT1G07350.1 encodes full-length SR45a protein with intact RS and RNA recognition motifs, while nonskipped variant AT1G07350.2 lacks the C-terminal RS region due to a frameshift in the alternative exon. Heat and drought stresses increased both transcript abundance and the proportion of exon-skipped transcripts encoding the full-length protein. We identified new splice sites and observed frequent intron retention flanking the alternative exon. • Conclusions: This study underlines the importance of visual inspection of RNA-seq alignments when investigating alternatively spliced genes. We showed that heat and dehydration stresses increase overall abundance of SR45a mRNA while also increasing production of transcripts encoding the full-length SR45a protein relative to other splice variants.American Journal of Botany 02/2012; 99(2):219-31. · 2.46 Impact Factor
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ABSTRACT: Alternative splicing (AS) of RNA is a key mechanism for diversification of the eukaryotic proteome. In this process, different mRNA transcripts can be produced through altered excision and/or inclusion of exons during processing of the pre-mRNA molecule. Since its discovery, AS has been shown to play roles in protein structure, function, and localization. Dysregulation of this process can result in disease phenotypes. Moreover, AS pathways are promising therapeutic targets for a number of diseases. Integral membrane proteins (MPs) represent a class of proteins that may be particularly amenable to regulation by alternative splicing because of the distinctive topological restraints associated with their folding, structure, trafficking, and function. Here, we review the impact of AS on MP form and function and the roles of AS in MP-related disorders such as Alzheimer's disease.Biochemistry 06/2012; 51(28):5541-56. · 3.38 Impact Factor
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ABSTRACT: Alternative splicing is now recognized as a major mechanism for transcriptome and proteome diversity in higher eukaryotes. Yet, its evolution is poorly understood. Most studies focus on the evolution of exons and introns at the gene level, while only few consider the evolution of transcripts. In this paper, we present a framework for transcript phylogenies where ancestral transcripts evolve along the gene tree by gains, losses, and mutation. We demonstrate the usefulness of our method on a set of 805 genes and two different topics. First, we improve a method for transcriptome reconstruction from ESTs (ASPic), then we study the evolution of function in transcripts. The use of transcript phylogenies allows us to double the specificity of ASPic, whereas results on the functional study reveal that conserved transcripts are more likely to share protein domains than functional sites. These studies validate our framework for the study of evolution in large collections of organisms from the perspective of transcripts; for this purpose, we developed and provide a new tool, TrEvoR.IEEE/ACM Transactions on Computational Biology and Bioinformatics 11/2012; · 1.54 Impact Factor
The Effects of Alternative Splicing on Transmembrane Proteins in the Mouse Genome
M.S. Cline, R. Shigeta, R.L. Wheeler, M.A. Siani-Rose, D. Kulp, and A.E. Loraine
Pacific Symposium on Biocomputing 9:17-28(2004)
THE EFFECTS OF ALTERNATIVE SPLICING ON
TRANSMEMBRANE PROTEINS IN THE MOUSE GENOME
M. S. CLINE, R. SHIGETA, R. L. WHEELER, M. A. SIANI-ROSE, D.
KULP, A. E. LORAINE
Affymetrix Inc., 6550 Vallejo Street, Suite 100
Emeryville, CA, 94608, USA
Alternative splicing is a major source of variety in mammalian mRNAs, yet many questions remain
on its downstream effects on protein function. To this end, we assessed the impact of gene structure
and splice variation on signal peptide and transmembrane regions in proteins. Transmembrane
proteins perform several key functions in cell signaling and transport, with their function tied
closely to their transmembrane architecture. Signal peptides and transmembrane regions both
provide key information on protein localization. Thus, any modification to such regions will likely
alter protein destination and function. We applied TMHMM and SignalP to a nonredundant set of
proteins, and assessed the effects of gene structure and alternative splicing on predicted
transmembrane and signal peptide regions. These regions were altered by alternative splicing in
roughly half of the cases studied. Transmembrane regions are divided by introns slightly less often
than expected given gene structure and transmembrane region size. However, the transmembrane
regions in single-pass transmembranes are divided substantially less often than expected. This
suggests that intron placement might be subject to some evolutionary pressure to preserve function
in these signaling proteins. The data described in this paper is available online at
Attention on alternative splicing has increased. Numerous groups have
published analyses estimating alternative splicing frequency [1, 2], and the
degree of conservation of splicing patterns [3, 4]. Consequently, alternative
splicing is now recognized as a major source of protein diversity in mammals.
Yet questions remain on its functional significance . A relation has been
observed between intron positions and compact units of protein tertiary structure
, and we previously observed that alternative splicing altered the pattern of
domains and motifs in roughly one third of the genes studied . Here, we focus
on protein motifs of distinct structural and functional relevance: signal peptides
and transmembrane helices. Thus, we explored the effects of gene structure and
splice variation on predictions by TMHMM  and SignalP .
TMHMM is the prevalent method for identifying putative transmembrane
helices in membrane-spanning proteins . These include transporters,
channels, and signaling proteins. SignalP is the prevalent method for predicting
signal sequences . Signal sequences help to guide secreted proteins into the
endoplasmic reticulum, and are frequently present in transmembrane proteins.
Because signal sequences and transmembrane regions are easily confused,
transmembrane and signal peptide predictors are best used together, with the
signal peptide predictor acting as a screen for the transmembrane predictor .
By analysis of genomic alignments, we identified the genomic coordinates
of a number of proteins, associating a gene structure with the protein sequence.
To focus our analysis on splice variation rather than genetic variation, we
derived putative protein translations from the genomic sequence. We then
applied SignalP and TMHMM to each translated protein, and determined the
genomic coordinates of each predicted signal and transmembrane region We
compared these genomic coordinates to the gene structures to determine how
often intron boundaries avoid transmembrane regions. For perspective, we
estimated how often intron boundaries might divide equivalently-sized segments
of the same protein, selected at random. Finally, we assessed how often splice
variation deletes or alters a signal peptide or transmembrane region of a protein.
Because of the significance of these regions, any such alterations will have major
consequences in protein localization and function.
2.1 . Gene structures and cDNA organization
We chose the mouse genome for this investigation to build upon and support
other investigations underway at our organization. We aligned all of the mouse
cDNA sequences from GenBank (release 128) to the mouse genome (Whitehead
Institute Center for Genome Research, April 2002) using blat . Of the 55997
sequences that aligned, we explored 13864 that aligned with coverage of at least
90% and a sequence identity of at least 95%; contained CDS annotations; and
had no cDNA inserts in alignment of the CDS regions to the genome.
Exon structures and transcript orientation were derived from the alignments
as follows. Successive segments of matching sequence were joined if they were
20 bases or closer; otherwise, they were considered introns. MRNA orientation
was determined by a weighted calculation on the directions inferred by the
labeled GenBank direction, the polyA site and signal evidence on the mRNA,
and the dinucleotide splice pairs derived from the genomic alignment.
We dynamically grouped transcripts together by gene according to their
exon structure. We considered two transcripts to be from the same gene if they
had overlapping genomic coordinates, and shared at least one intron junction.
We grouped these transcripts by splice variation as follows: if an intron in one
transcript alignment overlapped an exon in another, or if the two transcripts had
start or stop codons at different locations, then the transcripts are considered
products of different splice variants. Note that this scheme is not perfect: it
might miss cases where one transcript is a genuine longer form of another, with
additional exons outside the coding region. However, due to limitations in
sequencing technology, a cDNA sequence annotated as “full length” might not
necessarily represent the full length of the sequence. Consequently, we chose the
conservative route, and consider two sequences to be examples of the same
splice variant unless there is strong evidence that they are not.
Figure 1: Illustration of grouping transcripts by genes and splice variants.
Next, we pruned the gene set to ensure that no UniGene cluster was
associated with more than one gene. This step provided a safeguard against
bias due to a large population of paralogs. This generated a set of 13483
transcripts of 6847 genes and 8061 splice variants. From each splice variant, we
arbitrarily selected one protein for subsequent analysis.
Only 904 genes had multiple variants at the protein level. This should not
be regarded as an indication of alternative splicing frequency, as protein-level
evidence represents a high evidence standard. A greater degree of alternative
splicing can be observed by compiling putative transcripts from cDNA and EST
evidence , but such transcripts often have no clear protein translation.
2.2 Protein Sequence Analysis
For each cDNA sequence, we derived a protein sequence by assembling an
mRNA from the genomic sequence, and inferring a protein translation from its
CDS annotation. Note that this protein sequence might differ from the sequence
associated with the cDNA, as this scheme does not account for genetic variation.
This was deliberate. We chose to focus on splice variation. Other forms of
variation, including genetic variation, are outside the scope of this work.
Next, we applied TMHMM and SignalP to the translated proteins,
using default parameters for both. From the TMHMM output, we discarded
transmembrane regions with a score of 0.3 or less, or those that overlapped with
regions predicted as signal peptides. These methods allow identification of three
classes of proteins routed through the endoplasmic reticulum (ER). Proteins
which have a predicted signal peptide but no predicted cleavage may be routed
to the cell surface, but will remain anchored there; these are called Anchor
proteins. Those predicted signal peptides with a predicted peptide cleavage may
be released into the extracellular environment, and are denoted as secreted
proteins. Finally, transmembrane proteins bridge the cell membrane, but are not
released into the extracellular environment.
2.3 Genome-level analysis of protein transmembrane regions
Each transmembrane protein region was mapped to genomic coordinates
according to the CDS annotations of the associated cDNA and the protein
coordinates of the transmembrane region. Each transmembrane region was
divided into one more genomic spans, where a genomic span represents the
ungapped alignment of a protein segment onto the genomic sequence. If the
entire transmembrane region mapped onto one exon, then it had one genomic
span; if it was divided by an intron, then it had two genomic spans. For each
genomic span, we recorded its start and stop coordinates in the genomic
sequence and the protein sequence, and inferred the translation frame from the
corresponding CDS region.
Next, we divided the transmembrane regions into two sets: those appearing
in all transcripts of a gene, and those not. A region was placed into the first set
only if all transcripts contained a region of the same type (signal or
transmembrane), with the same genomic coordinates and translation frame.
ProtAnnot, a program designed to allow visualization of protein motifs in
the context of genomic sequence, was used to view protein sequence annotations
in the context of gene structures . The software is freely available from
Affymetrix at http://www.affymetrix.com/analysis/biotools/protannot/index.affx.
We applied SignalP and TMHMM to a nonredundant set of 8061 genome-
derived protein translations. 1156 proteins contained putative signal peptides,
and 1714 contained putative transmembrane segments. Altogether, 2039 of the
8061 proteins contained a transmembrane region of some form.
3.1 Relation between exon boundaries and transmembrane protein regions
Prior evidence suggests some correspondence between modules, compact sub-
units of protein domains, and intron boundaries . Along those lines, we would
expect intron boundaries to typically avoid transmembrane regions. Thus, we
assessed how often this is the case. Overall, intron boundaries did not split 695
of 1116 signal peptides (62.3%), 28 of 40 anchor peptides (70.0%), and 3628 of
5895 individual transmembrane regions (61.2%). The transmembrane regions
in single-pass transmembranes were divided by introns the least: 687 of 812
(84.6%) were not divided by introns. For seven-transmembrane proteins, 793 of
980 (81.2%) individual transmembrane regions in 120 proteins were not split by
introns. This follows the observation that genes encoding GPCRs, in particular,
consist of a small number of large exons .
To put this into perspective, we estimated the background likelihood of a
22-residue segment being divided by an intron, given observed gene structures
and transmembrane topologies. Note that 22 residues is the average length of a
region predicted by TMHMM. The likelihood estimation was as follows. For
each protein of n transmembrane regions, we identified the all positions in the
protein corresponding to a splice junction. Then, we selected n 22-residue
segments at random. If these n random 22-mers did not overlap, and were
separated by at least five residues (representing a minimal distance for turns
between adjacent transmembrane segments), then we noted the number of
segments placed n and the number m of segments that did not span any splice
junctions. This process was repeated 100,000 times to sample the protein’s
conformational space, yielding a total of N total segments placed, and M not
divided by introns. The likelihood l of a 22-mer segment being divided by an
intron, given the gene structure, was estimated as M/N. Finally, the overall
likelihood L of any 22-residue segment being divided in any K-pass
transmembrane was estimated as the average likelihood l for all K-TM proteins
analyzed. This data is shown in Figure 2.
In general, the likelihood that transmembrane regions are kept intact is only
slightly greater than background. Even the transmembrane regions in 7-TM
proteins are kept intact at a rate only slightly higher than expected, even though
they are kept intact at a high rate of 81.2%. 7-TM proteins tend to be encoded
by genes of few exons. This data indicates that transmembrane regions in 7-TM
proteins span introns infrequently because they have few introns, not because
introns are placed elsewhere in the gene. For contrast, the single transmembrane
region in 1-TM proteins is kept intact at a rate of 84.6%, versus a background
expectation of 58.5%. Thus, if there is some selective pressure to keep the
transmembrane regions intact in the genomic sequence, this is evidenced to the
greatest extent by single-pass transmembranes.
Number of transmembrane regions in the protein
Proportion of regions not
divided by introns
Transmembrane regions Random 22-mers
Figure 2: Shown by topology is the proportion of transmembrane regions not divided by an intron.
This is compared to the likelihood of a random 22-mer amino acid sequence not being divided by an
intron, as estimated by placing the equivalent number of 22-length segments on the protein
sequence at random in 100,000 trials per protein. The trend towards increased intact, single exon
TM sections for 5, 6, and 7TM proteins correlates well with the prevalence and importance of TM-
bound receptors, particularly for the large class of important GPCRs which contain 7TM segments.
3.2 Effects of alternative splicing on transmembrane protein regions
Previously, we analyzed all proteins with a plausible genomic alignment. Here,
we analyze only those proteins from 904 genes with protein-level evidence of
splice variation. Of the 904 genes, 240 contained some form of transmembrane
annotation. These genes yielded a total of 790 annotations in 553 distinct
proteins, each representing a distinct splice variant. We divided the annotations
into two sets: those common to all observed splice variants, and those not.
Annotations were considered common to all splice variants only if all variants
contained a region of the same type (signal or transmembrane), produced from
the same genomic coordinates and in the same translation frame. Additionally,
for an annotation to be common, we required the same class of annotation: the
same number of transmembrane spans for a TMHMM prediction, and the same
Anchor or Signal classification for SignalP predictions. As shown in Table 1,
alternative splicing was associated with changes in transmembrane topology for
about half of the genes studied, and about half of the annotations in each class.
Overall, 7-TM regions were altered by alternative splicing at a lower rate
than others, although the sample size is too small to suggest a significant trend.
We did not observe any general trends, such as whether the variants of a gene
tended differ in their their transmembrane span count by multiples of two, a
trend which would suggest that the terminal domains of the protein stayed in the
same cellular region even if the number of transmembrane spans varied.
Table 1: For each transmembrane architecture, listed are the total examples observed, and the
number that differ in some other variant of the same gene. Overall, half of the genes contained
splice variants with differing transmembrane architectures.
Topology Total Changed
Signal Peptide 145 79
1-pass TM 128 65
2-pass TM 17 15
3-pass TM 17 9
4-pass TM 15 13
5-pass TM 14 10
For all transmembrane proteins, the function of the protein is intrinsically
related to the number of transmembrane spans. Yet the effect is most vivid for
single-pass transmembranes. There are numerous documented cases of genes
with a single-pass transmembrane variant and a secreted variant; both variants
contain the same extracellular domain, and the secreted variant inhibits the
activity of the transmembrane variant. Two examples include the fibroblast
growth factor receptor 1 (FGF-R1)  and the neuropilins . Roughly half
of the single-pass transmembranes we analyzed contained a variant with no
transmembrane region. This data suggests that these cases might not be
examples of isolated phenomena, but part of a general trend.
In most cases when the transmembrane architecture was modified, one or
more transmembrane region was deleted. Yet in a small number of cases,
verified by hand, the genomic coordinates of one transmembrane region were
moved in one variant relative to another. Thus, the gene contained
transmembrane-coding regions in the exons not constitutively expressed; by
selective use of these regions, the splice variants contained the same
transmembrane composition. One example of this is MDR/TAP, the multi drug-
resistant ATP binding cassette, subfamily B. The splice variants of this gene
map to different 5’ exons, suggesting alternative promoters. Yet all variants
encode a signal peptide in the 5’ exons. So curiously, the presence of the signal
peptide is preserved in splice variation, even at the expense of maintaining two
different sets of genomic coordinates. Other genes showing similar behavior
include the interferon gamma receptor IFNGR, the poliovirus-receptor-related
gene PVR13, and the tyrosine kinase TYR03.
Anchor Peptide 7
10+ pass TM
3.3 Case Study1: Alternative splicing of GPCRs
GPCRs typically feature a simple gene structure, comprised of a small
number of large exons. Yet even so, they exhibit splice variation. Figure 4
shows the kappa-3 opiate receptor (KOR3) gene explored by Pan et al.  In
this gene, individual differences in splice variation are believed to have distinct
phenotypic consequences. Incomplete cross-tolerance, where patients are highly
tolerant of one opiate yet react to a second at surprisingly low doses, is believed
to stem from differences in splice variation.
Figure 3: Alternative splicing of the mu opiate GPCR. In this image generated by ProtAnnot,
the six splice variants for this gene are labeled with the letters a-f. Empty rectangles represent non-
coding exons. Filled rectangles represent translated exons, with the translation frame indicated by
the shade of grey. The small rectangles below each transcript indicate the locations of the
This gene has several documented splice variants: ordinary 7-TM GPCRs
(a); N-terminal anchored 1-TMs (b-d), and 4-TM variants with extracellular C-
terminal domains (e) . We observed a 6-TM variant in addition (f). Given
the complex interactions between membrane-bound receptors , the non-7-
TM receptors are not necessarily dead variants, but may be part of the complex
interplay between receptors in regulating response to outside influences and
3.4 Case Study 2: Alternative splicing and nonsense-mediated decay
In 30 randomly-selected genes, we found five examples in which alternative
splicing caused shifts in the translation frame and introduced premature
termination codons (PTCs). Although such events can stem from artifacts in the
cDNA library, we emphasize that all five sequences were documented as full-
length, with protein translations. The changes in translation frame stemmed
from shifts in the exon boundaries, and conditional inclusion or exclusion of
cassette exons. Two examples are shown in Figure 4.
Figure 4. Alternative splicing introduces premature termination codons (PTCs) via two
different mechanisms: variable splice site selection and optional inclusion of an alternative
exon in. In both examples, the termination codon in one of the transcripts is more than 50 bases
upstream of a splice junction, thus exposing it them to regulation by nonsense-mediated decay
pathway. (Top) TMC6 encodes putative 4-pass (a) and 6-pass (b) transmembrane membrane-bound
proteins are shown. The exon beneath the PTC contains a shorter 5’ leg in (a) than in (b), indicating
variation in the 5’ boundary of the affected exon in the two transcripts. (Bottom) Shown is Chnn
(calmin), a putative actin-binding protein. Inclusion of an optional exon in (a) introduces a PTC
which deletes a downstream single-pass transmembrane region present in (b).
Curiously, many of these PTC-containing variants contained splice junctions
downstream of the termination codon. According to current theories, this should
target these proteins for nonsense-mediated decay (NMD). After splicing,
components of the splicing machinery are thought to remain attached to the
mRNA near former splice junctions, marking the positions of former introns
. They are usually displaced during translation, but might not detach if the
mRNA contains splice boundaries 50 bases or more downstream from the
termination codon . Their presence is believed to activate the nonsense-
mediated decay pathway, resulting in degradation of the affected molecule.
The effects of NMD vary from gene to gene . Recently, it was proposed
as a genome-wide mechanism by which cells ensure splicing fidelity and avoid
the production of potentially toxic, nonfunctional proteins . Yet give our
results, are all classes of protein-coding transcripts equally susceptible to NMD?
We observed 3 examples of NMD-susceptible transmembrane protein encoding
transcripts (Tmc6, Clmm, Il17rb) in 30 genes examined. Perhaps mRNAs
encoding membrane-spanning proteins, which are co-translationally inserted into
the ER, might be subject to NMD to a lesser degree than other proteins.
Transmembrane proteins perform a number of key roles, including inter-cellular
signaling and transport. Their function is tied closely to their organization of
transmembrane spans. Alternative splicing modified this organization in about
half of the genes studied, almost certainly altering the functions of the proteins
produced. Thus, the process of alternative splicing could have a substantial
impact on any cellular processes in which these proteins are involved.
One cannot consider splicing without of gene structure. Associations have
been observed between exons and units of protein structure . Given the
functional importance of transmembrane regions, plus their short length, we
might expect them to be divided by introns rarely. On the surface, this seems
true. However, when compared to the likelihood of an intron dividing an
equivalently-sized protein segment, we observed that most transmembrane
regions were kept intact at a rate barely higher than expected. The exception is
the single pass in 1-TM proteins, which are kept intact far more frequently than
expected. Few protein regions have such clear functional interpretation as
these. There are numerous documented cases of 1-TMs with a secreted splice
variant, where the two variants contain the same extracellular domain and the
secreted variant inhibits the function of the transmembrane variant. These facts
together support the idea of an evolutionary mechanism that avoids
fragmentation of critical portions of the protein.
While this work represents a starting point. Here, our interpretation of the
results is limited by small data set sizes, resulting from the small amount of
cDNA data for the mouse. In future work, we are considering repeating this
analysis on other genomes where the cDNA data is more abundant.
Any analysis based on genomic data tells only half of the story. Any cDNA
sequence represents a splicing event that has been documented at least once.
The trends we reported here based on in-silico observations, but cannot describe
the conditions under which such trends arise. Questions remain, such as when
alternative splicing events are regulated, and when they represent random
consequences of a noisy process. Addressing such questions would require the
genomic data to be coupled with the proper measurement technology. In related
future work, we hope to shed more light on some of the events described here,
and the circumstances under which they occur.
We wish to thank several colleagues for insightful discussions on the science of
splicing: John Blume, Jing-Shan Hu, Gang Lu, Tyson Clark, Gangwu Mei,
Manny Ares, Chuck Sugnet, Bruce Conklin, Nathan Salomonis, and especially
Hui Wang. The analysis reported here would have been nearly impossible
without the elegant data analysis pipelines developed by Alan Williams, Brant
Wong, and Harley Gorrell. Additionally, we wish to thank Harley for his
generous assistance with the compute cluster and postgresql databases.
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