Group Title: BMC Genomics
Title: Application of DETECTER, an evolutionary genomic tool to analyze genetic variation, to the cystic fibrosis gene family
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Title: Application of DETECTER, an evolutionary genomic tool to analyze genetic variation, to the cystic fibrosis gene family
Physical Description: Book
Language: English
Creator: Gaucher, Eric A.
De Kee, Danny W.
Benner, Steven A.
Publisher: BMC Genomics
Publication Date: 2006
Abstract: BACKGROUND:The medical community requires computational tools that distinguish missense genetic differences having phenotypic impact within the vast number of sense mutations that do not. Tools that do this will become increasingly important for those seeking to use human genome sequence data to predict disease, make prognoses, and customize therapy to individual patients.RESULTS:An approach, termed DETECTER, is proposed to identify sites in a protein sequence where amino acid replacements are likely to have a significant effect on phenotype, including causing genetic disease. This approach uses a model-dependent tool to estimate the normalized replacement rate at individual sites in a protein sequence, based on a history of those sites extracted from an evolutionary analysis of the corresponding protein family. This tool identifies sites that have higher-than-average, average, or lower-than-average rates of change in the lineage leading to the sequence in the population of interest. The rates are then combined with sequence data to determine the likelihoods that particular amino acids were present at individual sites in the evolutionary history of the gene family. These likelihoods are used to predict whether any specific amino acid replacements, if introduced at the site in a modern human population, would have a significant impact on fitness. The DETECTER tool is used to analyze the cystic fibrosis transmembrane conductance regulator (CFTR) gene family.CONCLUSION:In this system, DETECTER retrodicts amino acid replacements associated with the cystic fibrosis disease with greater accuracy than alternative approaches. While this result validates this approach for this particular family of proteins only, the approach may be applicable to the analysis of polymorphisms generally, including SNPs in a human population.
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BMC Genomics

Bio.-l Central

Research article

Application of DETECTER, an evolutionary genomic tool to analyze
genetic variation, to the cystic fibrosis gene family
Eric A Gaucher* 1, Danny W De Keel and Steven A Benner2

Address: 'Foundation for Applied Molecular Evolution, Gainesville, FL USA and 2Department of Chemistry, University of Florida, Gainesville, FL
Email: Eric A Gaucher*; Danny W De Kee; Steven A Benner
* Corresponding author

Published: 07 March 2006
8MC Genomics2006, 7:44 doi:10.1186/1471-2164-7-44

Received: 08 December 2005
Accepted: 07 March 2006

This article is available from:
2006Gaucher et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: The medical community requires computational tools that distinguish missense
genetic differences having phenotypic impact within the vast number of sense mutations that do
not. Tools that do this will become increasingly important for those seeking to use human genome
sequence data to predict disease, make prognoses, and customize therapy to individual patients.
Results: An approach, termed DETECTER, is proposed to identify sites in a protein sequence
where amino acid replacements are likely to have a significant effect on phenotype, including causing
genetic disease. This approach uses a model-dependent tool to estimate the normalized
replacement rate at individual sites in a protein sequence, based on a history of those sites
extracted from an evolutionary analysis of the corresponding protein family. This tool identifies
sites that have higher-than-average, average, or lower-than-average rates of change in the lineage
leading to the sequence in the population of interest. The rates are then combined with sequence
data to determine the likelihood that particular amino acids were present at individual sites in the
evolutionary history of the gene family. These likelihood are used to predict whether any specific
amino acid replacements, if introduced at the site in a modern human population, would have a
significant impact on fitness. The DETECTER tool is used to analyze the cystic fibrosis
transmembrane conductance regulator (CFTR) gene family.
Conclusion: In this system, DETECTER retrodicts amino acid replacements associated with the
cystic fibrosis disease with greater accuracy than alternative approaches. While this result validates
this approach for this particular family of proteins only, the approach may be applicable to the
analysis of polymorphisms generally, including SNPs in a human population.

A comprehensive understanding of any system, biological
or non-biological, requires that we generate models for
both its structure and history. This truism applies to
genomics. The last decade has shown that an understand-
ing of history can improve, sometimes dramatically, our
understanding of the relation between the structure and

function in a protein family [ 1 ]. Examples of protein fam-
ilies that illustrate this include leptin, where a historical
analysis suggested that the mouse is an imperfect model
for human obesity [21, aromatase, where a historical anal-
ysis determined the physiological significance of three
enzymes evidently catalyzing the "same" reaction biosyn-
thesizing reproductive steroids in pigs [3], and angi-

Page 1 of 13
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otensin converting enzyme, where resurrection of
ancestral proteins provided insight into the specificity of
this protease involved in regulating blood pressure [4].

Probabilistic models for the history of a protein family
can be reconstructed from the amino acid sequences of
the currently extant descendents of that family. The recon-
struction starts with a multiple sequence alignment that
represents the evolutionary relation between individual
sites in the homologous family members, and a tree that
captures the familial relationships of the homologous
proteins themselves [5]. Computational heuristics then
infer the sequences of ancestral proteins throughout the
tree, at the same time as inferring nucleotide and amino
acid replacements that occurred along individual
branches of the tree.

Amino acids in proteins continue to be replaced in the
contemporary world. Although individuals within a pop-
ulation are genetically far more similar than they are dif-
ferent, genetic differences underlie many of the
physiological differences between individuals. They are
also responsible for many diseases and variable responses
of different individuals to their clinical therapies.

The ability to predict which mutations cause disease, or
differences in how individuals respond to standard medi-
cal protocols, will rely on detailed characterizations of
mutations. For missense (non-synonymous) changes in
the coding regions of genes, the descriptions include the
locations of mutations on a protein, the physico-chemical
properties of the amino acid replacements, rates of muta-
tion at sites based on comparisons of homologous
sequences, and probabilities of inferred ancestral amino
acid states during the evolutionary history for the gene of
interest. These descriptions are commonly combined
within the field of molecular evolution, while only
recently have they been integrated for the medical sci-

In preparation for the accumulation of human genome
mutation information from single-nucleotide polymor-
phism databases (SNPs), the medical community will
require models that incorporate the descriptions listed
above in hopes of generating accurate predictions of toler-
ated and non-tolerated amino acid replacements within
the human population. This will be a necessary step to
fully use genomics as part of predictive and personalized
medicines [6-8].

The role of genetic variation in human disease is exempli-
fied by the disease cluster known as cystic fibrosis (CF). CF
causes tragic and debilitating phenotypes in the pulmo-
nary and gastrointestinal tract of patients that it afflicts.
The protein most closely associated with this cluster is the

cystic fibrosis transmembrane conductance regulator
(CFTR). CFTR pumps chloride ions across the cellular
membranes of lung, liver, digestive and reproductive
tracts, pancreas, and skin tissues, inter alia, maintaining
the hydration of extracellular secretions.

Structurally, the CFTR protein is an ATP-Binding Cassette
(ABC) transporter protein that, in humans, is a peptide
1480 residues in length (~168 kDa) encoded by a gene on
chromosome 7 with 6129 nucleotides [9-11]. The protein
has five domains. Two of these domains span the mem-
brane (MSDs); each of these comprises 6 transmembrane
helices that form a chloride ion channel. The CFTR also
has two nucleotide-binding domains (NBDs) that bind
and hydrolyze ATP, and a regulatory domain.

Missense mutations in the membrane-spanning domains
of CFTR are the molecular etiology of the disease in many
cystic fibrosis patients [12-17]. The Cystic Fibrosis Muta-
tion Database collects 108 of these mutations [18]. The
database does not record mutations that do not create the
disease, unless multiple variations (which need not all be
responsible for the disease) are present in a single diseased
patient. This makes the database a valuable resource for
testing new ideas to identify variation that might be the
source of disease.

Here, we introduce a new approach to determine whether
an amino acid replacement at a site in a protein is more or
less likely to have a significant impact on fitness, includ-
ing causing a disease. The model attempts to detect muta-
tions that lead to clinical diseases regardless of the
mutation's role in recessive and dominant patterns of
inheritance [19]. In this manner, the approach can also
identify heterozygous recessive changes, with the poten-
tial to cause disease, within a carrier background.

Our approach exploits contemporary sequence data to
reconstruct the evolutionary history of the site using
model-dependent mathematical heuristics. The approach
then identifies sites that have historical normalized
replacement rates that are higher, average, or lower than
the typical site in the protein. It then infers the likelihood
that any specific amino acid was present at that site over
the period of history defined by the tree. Sites having
lower than average historical amino acid replacement
rates are hypothesized as being sites most likely to hold
phenotypically significant changes in a modem popula-
tion. Amino acids that have a low probability of having
been present at that site during the evolutionary history
are more likely to have phenotypic impact if found in the
present day population. We test this approach using the
CFTR protein family as a model.

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BMC Genomics 2006, 7:44


CFTR orthologs
all ABC transporters

Figure I
Phylogenetic schematic highlighting the relationships between
ABC transporters, mammalian CFTRs, and human CFTR pol-
ymorphisms based on sequence variation.

Results and discussion
Many ideas for predicting whether an amino acid replace-
ment is likely to have a significant impact on the pheno-
type of an extant protein are based on the notion that
amino acid X at site i will be tolerated in a current popu-
lation if, and only if, X was tolerated historically in a pred-
ecessor population. A less stringent application of the
same notion asks whether X has been tolerated in a
homologous protein that is not a direct ancestor of the
current population of interest (Figure 1), but is related as
a "distant cousin". This notion can be made still weaker
by constructing it in probabilistic form ("Xi is more likely
to be tolerated if it appears in an ancestor, or in a distant
cousin"). Further, the probabilities might be parameter-
ized depending on the amino acid replacement being con-

This notion is both obvious and fully logical in certain
cases. For example, if an active site histidine is required at
position 12 in a protein and required for catalytic activity,
and if the catalytic activity provided by that histidine in
that protein is required to confer fitness on the host, then
replacing His12 by any of the other 19 amino acids will
cause a disease in the modem host. Further, the replace-
ment will not have occurred in the past, as the mutation
behind the replacement would not have been fixed in the

population. Any individual having it would have lower
fitness, and would not have passed that replacement on to
a population of descendents.

Chemical considerations suggest that the situation must
be more complex than implied by this simple model. For
example, good reasons exist to suspect that whether Xi is
tolerated by a population in an extant protein depends, at
least in some cases, on what amino acid is present at other
sites j. Further, we may suspect that if multiple sites j are
different in the cousin subfamilies, inter-site interactions,
difficult to capture in any analysis might allow an amino
acid to be tolerated in a cousin even though it is not toler-
ated at site i in the protein of interest [20].

Further complicating the model is the recognition that
proteins are frequently recruited to have different func-
tional phenotypes. In the example discussed above, if the
catalytically active protein evolved from a protein whose
role required no catalysis in an earlier period of evolution-
ary history, then His 12 may not have been present in that
period, even though its absence in a modem protein
might cause disease.

The SIFT strategy
This type of evolutionary analysis underlies a tool recently
introduced by Ng and Henikoff. Known as SIFT (sorting
intolerant from tolerant) [21-231, the tool constructs a
profile for every site in a protein from a set of input
homologous protein sequences. This profile reports a
probability for each of the 20 amino acids being at that
site in the generic homolog. A replacement in a contem-
porary population introducing amino acid Xi is viewed as
"tolerable" if that normalized probability forXi in the pro-
file is greater than 0.05.

For SIFT, the input proteins can be obtained from a search
using PSI-BLAST (position specific iterative-basic local
alignment search tool). The cutoff to determine homolo-
gous sequences is a position-specific conservation (read,
distance) metric for homologs based on classical informa-
tion theory (log220). Alternatively, a user can define the
input dataset of homologous sequences for SIFT analysis
(as done in the present study).

Recognizing the possibility that the database sequences
might not carry, at any particular site, all of the amino
acids that are in fact present at that site in all extant
sequences on the planet, Ng and Henikoff add "pseudo-
counts" to the data. The number of these is based on an
application of 13-component Dirichlet mixtures. Addi-
tional pseudocounts are then added based on an expo-
nential derived from a diversity metric that includes the
numerical rank (an integer from 1 to 20) for each of the
amino acids at each site.

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BMC Genomics 2006, 7:44


i-- Salmon

Figure 2
Phylogeny of the CFTR family. Bootstrap values a
at corresponding nodes of the tree. Scale bar rej
amino acid replacements per site per unit evolut

The DETECTER strategy
It is not necessary to use such a heuristic a
model sequence diversity in a family of pro
cially given the availability of many empire
tools for modeling the historical divergence
sequence from descendent sequences. We asl
a tool that captures, in a more formal way, th
ary relationship between the input sequences
better means to identify phenotypically signi

Under the acronym DETECTER (Determining
relevant Transmutations using Evolutionary -
the tool exploits an alignment of homologou
evolutionary models of DNA substitutions or
replacements, phylogenetic analyses, and pr
ancestral character states throughout the his
gene family. These are obtained using a model
lihood method devised for reconstruction
sequences, and implemented in PAML [24],
case. This method uses standard statistical th
erate the posterior probabilities of different
tions given the data at a site [25-27].

For each site in the protein sequence, posterior
all 20 amino acids are calculated and represei
ability of an given amino acid having been at
the protein during its evolutionary history. T
are calculated from patterns of amino acids i
ment, models of sequence evolution, phylogei
lengths, and site-specific replacement rates.
replacements having posterior probabilities
or equal to 0.05 are considered tolerated in

population, while those less than 0.05 are considered
Sheep non-tolerated and may lead to aberrant protein behavior.
)Bovine The tools' main differences lie in their abilities to estimate
rabbitt whether a site is conserved or rapidly evolving, a site's pro-
manhesus pensity to accept amino acid replacements and how much
hesus change has occurred at a site. SIFT estimates these from
aboon pseudocounts, diversity metrics based on sequence con-
S- Rat servation, and Dirichlet mixtures, while DETECTER esti-
-- Mouse mates these from the evolutionary analyses discussed
Xenopus above.

We applied SIFT and DETECTER to the CFTR family and,
separately, to related ABC transporters. These were
obtained from public databases and the MasterCatalog
[28]. Our analyses relied on an evolutionary tree built
using the topology search tools in PAUP with complete
re indicated CFTR genes [29]. The robustness of the resulting phylog-
presents eny was estimated through bootstrap analysis; it is con-
ionary time. sistent with a species tree for these organisms (Figure 2).
The CFTR sequences and topology were subsequently
used for a maximum likelihood analysis in the PAML phy-
logenetic package [30].
approach to
teins, espe- Structural analysis of the CFTR mutations in the database
ically based The disease-causing mutations in the 12 helices of the two
of protein membrane spanning domains (MSD) of CFTR have been
ced whether analyzed by several groups from a structural perspective
e evolution- [12,15,17]. The majority (74%) of these sites in the
might be a human sequence are occupied by hydrophobic (non-
ficant varia- polar) amino acids (FAMILYVW). Moderately polar
(CPGST) and highly polar (KRENDQH) residues are
found in the remaining 19% and 7% of the sites, respec-
g Clinically- tively.
ssequences, Schiffer-Edmundson helical wheels revealed the spatial
amino acid relation of sites holding the highly polar residues in the
edictions of transmembrane helices (data not shown). As has been
story of the noted in membrane spanning helices generally, polar res-
1-based like- idues are not randomly disposed around the helix. Rather,
g ancestral nearly half of the highly polar residues in the CFTR trans-
for our test membrane regions are spaced 3-4 sites from each other,
eory to gen- and therefore present their side chains to the same side of
reconstruc- the helix. This suggests that these residues participate in
electrostatic linkages necessary for structure/function rela-
r values for
nt the prob- Of the highly polar residues in the MSDs of the native
that site in CFTR protein, arginine appears to be special. Arginine is
these values found here at the ends of the alpha helices in which it
n the align- resides. There, it may help anchor the helices [16].
netic branch
Amino acid Of the >500 disease-causing replacements in the CFTR
greater than protein from the human population, 108 are found in 83
the modern of the 253 sites in the membrane-spanning domains

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BMC Genomics 2006, 7:44

15 -

12 -


Ala Arg Asn Asp Cys Gin Glu Gly His lie Leu Lys Met Phe Pro Ser Thr Trp Tyr Val
1.0 -

0.8 -

0.6 -

Ala Arg Asn Asp Cys Gin Glu Gly His lie Leu Lys Met Phe Pro Ser Thr Trp Tyr Val

15 -

12 -



Ala Arg Asn Asp Cys Gin Glu Gly His lie Leu Lys Met Phe Pro Ser Thr Trp Tyr Val

Figure 3
Frequencies of amino acids lost and gained through missense mutations of the CFTR protein. A, Observed frequencies of
native CFTR amino acids lost through phenotypic missense mutations. B, Same as A but normalized for the particular frequen-
cies of amino acids present in the membrane-spanning domains. C, Observed frequencies of non-native amino acids gained
through phenotypic missense mutations from the CFTR mutation database.

(MSDs). Structural analysis shows that the majority of amino acids therefore gives a better understanding of the
these replacements are found in sites that hold non-polar types of phenotypic changes leading to cystic fibrosis (Fig-
amino acids in the native, non-disease form of the pro- ure 3). The normalization shows that the loss of a polar
tein. This is expected, given that most sites in the MSDs residue (Asp, His, Pro, Gly, Arg, Asn, Cys, Gln, and Glu)
hold non-polar amino acids in the archetypal sequence. has a greater chance of being associated with the disease
Normalizing with respect to the native frequencies of (per polar residue) than the loss of a hydrophobic residue.

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BMC Genomics 2006, 7:44






Figure 4
Phylogeny of the ABC-family membrane-spanning domains
from PFAM.

This observation is consistent with both the known struc-
tural and functional importance of polar residues in trans-
membrane regions and previous studies that analyzed the
Human Gene Mutation Database [121. The loss of native
Met and Tyr are exceptions (Figure 3b).

Providing the potential to form salt-bridge and/or hydro-
gen bonds within the MSDs can also be associated with
the disease. The observed gain of amino acids that offer
these properties support this view (Figure 3c). The most
prevalent residues gained exhibit salt-bridge and H-bond-
ing potential (Arg, Cys, Trp, Ser, Asp, Glu, Lys and Thr).
The frequent gain of Pro, Leu and Ile do not support this
view. Proline, however, is justified by its propensity to
break helices based on its lack of backbone H-bonding
potential, although other explanations may be required

Building evolutionary models for CFTR proteins
To add a historical dimension to these structural observa-
tions, we exploit an evolutionary analysis. The analysis
begins with the (perhaps naive) hypothesis that sites
where replacement is likely to have a detrimental impact
on fitness evolve more slowly than sites where replace-
ment does not [32]. This suggests that one might be able
to retrodict disease-causing amino acid replacements in
CFTR by identifying sites that have historically evolved
more slowly in the protein family.

To test this hypotheses, we needed to build an evolution-
ary model. Recognizing that alternative theories of evolu-
tion generate different models, we explored alternative
datasets and parameters.

We began by retrieving a seed multiple sequence align-
ment for the transmembrane regions of ABC transporters
(including many not classified as CFTRs) from Pfam [33].
An amino acid replacement rate matrix for this dataset
was estimated in PAML from the phylogeny shown in fig-
ure 4. The resulting transmembrane matrix (TM) was
incorporated into subsequent phylogenetic analyses dis-
cussed below and compared to results obtained using a
replacement matrix specific for globular proteins, Jones-
Taylor-Thornton (iTT) [34].

Two datasets of CFTR sequences were subsequently con-
structed. The first comprised the sequences of the com-
plete CFTR protein sequences. The second comprised only
those parts of the sequences that formed the membrane
spanning domains. The different replacement probabili-
ties and trees with different branch lengths generated by
these different analyses were then compared. For its abil-
ity to infer branch lengths and ancestral states, the iTT
matrix (not specifically designed for membrane-embed-
ded protein sequences) is expected to outperform the TM
matrix for the complete CFTR dataset, as only 253 sites
out of the ca. 1450 total sites are in contact with the lipid
bilayer. Thus, a majority (roughly 83%) of the sites in the
CFTR protein are expected to evolve like most globular
proteins, and therefore have their amino acid replacement
best represented by the iTT matrix. Consistent with this
expectation, likelihood scores for the evolutionary models
for the complete CFTR dataset were 13825.91 and
14740.58 for the ITT and TM matrices, respectively.

Alternatively, the TM matrix is expected to outperform the
iTT matrix for the MSD-only dataset, in part because the
TM matrix was based on these membrane spanning
domains. The likelihood scores for these datasets were
2122.61 and 2019.63 for the ITT and TM matrices, respec-
tively. A second-order Akaike Information Criterion
(AICc) test fitting the two matrices to the data supported
this expectation (AAIC = 205.9592).

Testing the hypothesis that polymorphisms in slowly
evolving sites are more likely to be associated with disease
To estimate the historical rates of replacement, a tool
implemented by Yang within the PAML package (v3.14)
was used. We exploited this tool's ability to examine an
entire protein sequence family and generate, for each site,
a normalized replacement rate based on the posterior
mean probabilities of the site's extant and historical
amino acid patterns residing in the individual categories
of the gamma distribution. These numbers indicate the
rate of replacement at the site throughout the history of
the family, normalized so that the average replacement
rate is unity. Thus, no site can have a normalized replace-
ment rate below 0, but sites can (in principal) be substan-
tially above unity. In the CFTR protein, the highest

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BMC Genomics 2006, 7:44



6 **


t ee*4* m4e * *

0.5 1 1.5 2 2.5 3
Site-specific amino acid replacement rates






C -,

Figure 5
Correlation of the number of missense mutations versus the
estimated site-specific amino acid replacement rate hosting
the mutations.

normalized replacement rate is ca. 2.8 replacements/site/
unit evolutionary time.

Once normalized replacement rates were calculated for
each site, we identified sites that have had higher-than-
average, average, and lower than average replacement
rates in the history of the membrane-spanning domains
of CFTR. Based on the hypothesis, we expected that sites
having lower-than-average historical replacement rates to
be more likely (than the average site) to hold polymor-
phisms in human populations associated with CF. Con-
versely, we expected that sites having higher than average
historical replacement rates to be less likely to hold poly-
morphisms associated with CF.

This proved to be the case. Only 42% of the sites (106
sites) have a historical replacement rate greater than unity.
In contrast, 58% of the sites (147 sites) have a historical
replacement rate less than unity. Of the 108 phenotypi-
cally significant mutations in the database, however, 74%
are in sites that have a below average historical replace-
ment rate (Figure 5).

The correlation was supported more strongly by distribut-
ing the sites into six bins based on their normalized
replacement rates (Table 1), and noting that those in the
bin with the lowest normalized replacement rates (nor-
malized replacement rate 0.0-0.5, 77% of the sites here
host a CF mutation) were more likely to be associated
with the disease than those in the next higher bin (0.5-
1.0, 44% host a CF mutation), and that this trend contin-
ued to the highest bin (2.0-2.5, 17%, chi-square = 22.1
probability = 0.001).

14- 6 3
+ + 8

2 7
0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5
Average Replacement Rate for Individual Helices

Figure 6
Correlation of the number of missense mutations versus the
average replacement rate for the individual alpha helical
transmembrane region hosting the corresponding mutations.
Transmembrane helices 1-12 are numbered.

The predictive power using evolutionary rates can be
extended to the 12 individual transmembrane helices of
the CFTR protein. The correlation of the number of phe-
notypic missense mutations per individual helix to the
average replacement rate of that helix highlights patterns
not seen in the absence of evolutionary analyses (Figure
6). Specifically, the helices hosting a greater proportion of
slowly evolving sites are more likely to give rise to pheno-
typic missense mutations. Supporting this view, the six
slowest evolving helices host a total of 72 CF missense
mutations (67%), while the six fastest evolving helices
host only 36 missense mutations (33%). Further, since
five of the six slowest evolving helices reside in MSD 1,
mutations in this membrane spanning domain appear to
be more deleterious than mutations in MSD 2.

Extending the analysis to include the history of amino acid
We then enhanced the analysis by considering the specific
amino acids involved, including those inferred at individ-
ual sites throughout the evolutionary history of the CFTR
family. The CFTR topology and the TM matrix were used
to estimate ancestral character states at all the internal
nodes of the phylogeny (Figures 2, 4). The likelihood
associated with any amino acids having been present dur-
ing the history of CFTR were collected for each position of
the multiple sequence alignment. Any amino acid residue
having a posterior probability greater than 0.05 at any
internal node of the tree for a given site was then predicted
to be tolerated in the modem protein. This cutoff is arbi-
trary, but was chosen to be consistent with the cutoff used
by Ng and Henikoff.

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BMC Genomics 2006, 7:44

Table I: Distribution of CF-causing mutations and the individual amino acid replacement rates of the sites hosting these mutations.

replacement rate

> 2.5

%of sites


Sites with
significant mutations


Expected sites if
randomly distributed


In drawing inferences about ancestral sequences, it is also
important to be selective about what extant homologs to
include in the analysis. As noted above, subfamilies
within a large family of homologous proteins need not
have the same "functions", but might play very different
roles as a consequence of recruitment in the historical
past. As has been discussed by many, subfamilies within a
family recruited to perform different functions divergently
evolve with different patterns of sequence evolution. In
particular, there is no reason to expect that sites that have
high replacement rates in one subfamily are the same as
sites that have high rates of replacement in another [35],
or that the patterns of replacement in an ancestral popu-
lation where the function was different from the function
in the modem family will accurately identify phenotypi-
cally significant variation in the modem family having the
derived function.

To recognize these realities, the DETECTER tool was
applied to various datasets chosen to deliberately include,
or deliberately exclude, subfamilies within the ABC trans-
porter family that had roles different from the role played
by CFTRs. Separately considered were: MSD domains for
CFTR-only [this same dataset was analyzed by DETECTER
(Dl) and SIFT] and ABC transporters [analyzed using
DETECTER only (D2)].

Table 2 outlines the predictions of tolerated amino acid
replacements made by DETECTER (Dl and D2) and SIFT.
These were compared to the amino acid replacements
associated with the CF disease in the Cystic Fibrosis Muta-
tion Database.

The approaches performed similarly, but their differences
are noteworthy. Of the 108 known mutations in the CFTR
membrane spanning domains, DETECTER incorrectly
predicted 8 of these mutations to be tolerated, when its
construction of the history of the site was based on the Dl
dataset. These constitute error, as these 8 are believed to
cause disease in the human population. Thus, the
DETECTER approach created ~8% false negatives. The

coefficient between the replacement rate at any individual
site versus the number of predicted tolerated amino acids
highlighted a positive correlation (Pearson r = 0.85).

The SIFT approach, however, generated more false nega-
tives when analyzing the same MSD-only dataset. This
analysis predicted that 15 of the 108 mutations that are
associated with the CF disease would be tolerated.

Last, the DETECTER tool applied to the ABC transporter
dataset (D2) generated the largest number of false nega-
tives. After considering the evolutionary history of a data-
set that included ABC transporters that had functionally
diverged from the CFTR role, the DETECTER tool mispre-
dicted that 47 of the 108 mutations in the MSDs would be

These results show the value of a historical analysis of pro-
tein sequence variation and an associated disease, cystic
fibrosis. Further, they show the hazards of applying a his-
torical analysis naively, across a family of proteins where
the physiological role has itself diverged.

Some of the differences in outcome can be directly attrib-
uted to the incorporation, in a historical analysis, of pro-
teins that do not play the same physiological role as the
protein of interest (here, CFTRs).

Thus, the D 1 and D2 analyses differ in that the former nar-
rowly includes only those proteins that serve as CFTRs,
while the latter includes ABC orthologs that do not. This
is undoubtedly the cause of the large number of false neg-
atives that arise when the DETECTER tool is applied to the
ABC transporters as a whole, and the very few of false neg-
atives that arise when the DETECTER tool is applied to
CFTRs only.

Differences in the outcomes between SIFT and DETECTER
(D 1 dataset) reported above are not explained in this way.
For example, SIFT incorrectly predicts that replacements at
positions 209 (A->S), 1006 (A->E), and 1148 (N->K) will

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BMC Genomics 2006, 7:44

be tolerated. In fact, each of these is associated with the CF
disease, and none are incorrectly predicted to be tolerated
by the DETECTER tool applied to either of the datasets.
Two of these replacements involve apolar-to-polar
changes, while the other is polar-to-polar. The site-specific
replacement rates of these positions alone (2.42, 2.62,
and 0.87, respectively) do not offer much insight.

Interestingly, while the use of different amino acid
replacement matrices (TM and ITT) by DETECTER had
moderate effects on the overall probabilities of the
inferred ancestral character states, in no instance did this
affect the overall predicted tolerability of any amino acid
replacement (data not shown).

Table 2: Comparing tolerated amino acid replacement predictions using the DETECTER and SIFT approaches to the Cystic Fibrosis
Mutation Database

Site Residues SIFT DI

D2 Site Residues SIFT DI


X(V) 336
X 338
X 347
X 866
T(R) 912
X 913
X 917
X 919
X(T,V) 920
X 924
X(Y) 993
X 994
X(S) 1005
X 1014
X 1105
X 1118
(T,G,V) 1130
X 1136
X(V) 1137
X 1139
X 1140
X(V,F) 1142
X 1147



D I and D2 refer to CFTR MSD-only and ABC-homolog sequence analyses using the DETECTER approach. SIFT was applied to same dataset as in
the DI analysis.
Position numbers correspond to the human CFTR protein.
Residues listed are extracted from the Cystic Fibrosis Mutation Database.
X indicates that the predicted tolerated amino acid residue is found in the Cystic Fibrosis Mutation Database. Residues in parentheses indicate
which predicted tolerated amino acid is referred to when a site hosts multiple missense mutations.

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BMC Genomics 2006, 7:44

All eight amino acid replacements incorrectly predicted by
analysis using the DETECTER tool applied in Dl are
among the 15 incorrect predictions made by SIFT. Four of
these involved apolar-to-apolar replacements (I->V twice,
M->I, and V-I), three moderately polar to apolar replace-
ments (P->L twice, and S->L) and one moderately polar
to highly polar replacement (G->R). Previous studies have
classified this last replacement as having a high 'pheno-
typic propensity' for disease [14,16], and in fact occurs
four times in the Cystic Fibrosis Mutation Database. Both
the DETECTER and SIFT approaches incorrectly predict
the G->R replacement to be tolerated at position 219
because the close homologXenopus has an Arg at this posi-
tion. The tolerance of Arg at this position in Xenopus may
be due to altered selective constraints or altered structural
bonding patterns acquired to compensate for the loss of
Gly at the site in this species.

Such altered selective constraints may also provide an
explanation for some of the false-negatives inferred by
DETECTER. Here, 4 out of the 8 sites represent cases in
which the residue at the site is conserved for the mamma-
lian sequences, and conserved-but-different for the non-
mammalian sequences. Conserved-but-different patterns
are often invoked to explain functional divergence
between biomolecules [36]. Further, these four sites reside
in helices that host the majority of CF missense mutations
and have slower than average replacement rates (Figure
6). Thus, while amino acid replacements were tolerated
during the divergence of mammalian and non-mamma-
lian species, further replacements appear to be non-toler-
ated within any single subclade. Here, the combination of
output from DETECTER and evolutionary rates provides
additional information to draw conclusions on the toler-
ance of mutations.

Alternatively, the other half of the 8 incorrect predictions
generated by DETECTER are amino acid replacements
present in CF patients that also host other mutations
implicated in causing the disease. These amino acid
replacements may thus represent neutral polymorphisms
carried within the disease background. As such, some of
the apparent false predictions by DETECTER may in fact
be true negatives.

While statistical analyses targeted against genomic
sequence databases are important in developing validated
tools for use in bioinformatics, many of the most impor-
tant concepts that have driven the field have emerged
through the analysis of individual cases [37-391. This is
not surprising, given that proteins are organic molecules.
Understanding in organic chemistry has nearly always
come through the development of narratives based on
case studies, where the concepts in those narratives have

then been tested, modified, and expanded through the
addition of further narratives. As with structure-function
relations in organic molecules, structure-function analysis
in proteins asks how changes in the arrangement of atoms
in a protein changes its properties.

Examination of a single dataset for a single protein family
does, however, have certain disadvantages. Most obvi-
ously, the approach is validated for that family only. Fur-
ther, there is the risk that this family is peculiar with
respect to families generally, and approaches that work
here will not work generally.

These concerns aside, it is clear that adding evolutionary
information to the structural information in the cystic
fibrosis family provides new insights. Seventy (65%) of
the 108 phenotypic missense mutations residing in the
membrane-spanning domains of CFTR involve inter-class
switches between apolar, moderately-, and highly-polar
residues. Due to the high proportion of hydrophobic res-
idues in the MDS, it was not surprising that phenotypic
missense mutations of native apolar residues were respon-
sible for the majority of mutations leading to cystic fibro-

Loss of native highly polar residues through phenotypic
mutations, however, represented the largest proportion of
mutations as a percentage of class. There were 17 pheno-
typic missense mutations associated with the 18 native
highly polar residues located within the MSDs. This indi-
cates that the physico-chemical properties of apolar resi-
dues provide specific and necessary structural and
functional hydrogen bonding interactions in the MSDs.

Along similar lines, the observation that apolar-to-polar
amino acid replacements comprised the largest observed
number of inter- or intra-class missense mutations is con-
sistent with the role of hydrogen bonding patterns in the
membrane-spanning domains. Here, the addition of H-
bonds can result in undesired interhelical crosslinks, dis-
ruption of active and/or regulatory sites, and modified
helical packing through steric hindrances [14,16,17].
Forty-two of the 108 missense mutations involve apolar-
to-polar replacements.

The DETECTER and SIFT approaches generate comparable
predictions regarding the tolerability of phenotypic mis-
sense mutations in the CFTR protein, i.e., differentiating
true negative (correctly-predicted tolerated) from false-
negative (incorrectly-predicted tolerated) amino acid
replacements. Notable exceptions, however, are apparent
and most likely explained by the different approaches of
the two programs. DETECTER relies on phylogenetic anal-
yses and invokes models of sequence evolution tailored
for specific gene families, while SIFT relies on its ability to

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BMC Genomics 2006, 7:44

capture models of sequence evolution indirectly through
sequence alignments only. Thus, SIFT generates pseudo-
counts from a Dirichlet mixture to estimate expected
(unobserved) sequence diversity, whereas DETECTER
attempts to capture this information through branch
lengths and implicit models of sequence evolution using
phylogenetic analysis.

We have demonstrated the importance of capturing fea-
tures of an amino acid replacement matrix (e.g., apolar-to-
polar changes), site-specific evolutionary rates (validating
the notion that changes in slowly evolving sites care cor-
related with disease states), homologous sequence diver-
gence (close relatives versus distant cousins) for the
DETECTER approach to predict the consequences of pol-
ymorphisms in the coding regions of CFTRs in the human
population. While none of these analyses are unique
when considered alone, their combination is unique and
may represents an important contribution to clinical diag-

Additional studies are required to differentiate the abili-
ties of DETECTER and SIFT to discriminate true-positive
(correctly predicted to be non-tolerated) from false-posi-
tive (incorrectly predicted to be non-tolerated) amino
acid replacements for CFTR. Advances in technology ena-
bling the collection of large amounts of SNP data will
undoubtedly allow these studies to be performed in the
near future, and allow the comparison of different meth-
odologies such as DETECTER, SIFT, and POLYPHEN
along these lines [23,40].

It is noteworthy that SIFT and POLYPHEN have analyzed
data from other mutation databases such as the human
non-synonymous single nucleotide polymorphism data-
base [22,40,411. Although previous studies using SIFT
have demonstrated its ability to outperform analyses
attempting to predict tolerated amino acid replacements
based on scoring matrices alone, these studies highlight
the need for algorithmic development to improve accu-
rate predictions of non-tolerated (deleterious) amino acid
replacements [21,22,421. Additional studies will also be
required to understand how sequence sample- and popu-
lation-sizes affect the predictions of tolerated and non-tol-
erated amino acid replacements.

We have shown that incorporating models of molecular
evolution to generate statements about tolerability of mis-
sense mutations can enhance the power of predictive
medicine. These statements are even more powerful when
correlated with known three-dimensional structural infor-
mation [2,3,35,40,41,43-461. For this reason, we expect
that the structure of CFTR will provide added value to
such analyses. The genomic medicine of the future will
require both reliable predictions about which types of

mutations cause disease (predictive medicine) and
detailed understandings of the variation in different
human subpopulation's responses to therapeutics (per-
sonalized medicine) [6-8].

Sequence data
Complete CFTR genes were collected from the Genbank
database and aligned: Homo sapiens, human (gi: 1809238);
Macaca mulatta, rhesus (gi:3047171); Papio anubis,
baboon (gi:5679281); Oryctolagus cuniculus, rabbit
(gi:7442654); Ovis aries, sheep (gi:2506121); Bos taurus,
bovine (gi:461719); Rattus norvegicus, rat (gi:34854998);
Mus musculus, mouse (gi:20141218); Xenopus laevis, frog
(gi:1617482); Bufo bufo, toad (gi:12963887); Salmo salar,
salmon (gi:12746235); Takifugu rubripes, blowfish
(gi:38322733); and Fundulus heteroclitus, killifish
(gi:3015540). Additional CFTR sequences have been
deposited in Genbank since we initiated our studies [47].
These sequences are not expected to affect our evolution-
ary analyses since we calculated relative rates opposed to
absolute rates.

ABC transporter homologs of CFTR were identified by a
BLAST [481 search using default parameters. Ninety seven
full length sequences were retrieved and aligned. Multiple
sequence alignments (MSA) were conducted by ClustalW
[49], with minor adjustments made by hand.

Amino acid replacement matrix
The ABC transporter membrane spanning domain was
retrieved from the PFAM [33] database (PF00664). The
multiple sequence alignment corresponding to the 'seed'
was cropped from 73 to 62 sequences due to inappropri-
ate gapping. This multiple sequence alignment was used
to estimate the transmembrane rate matrix (TM) using
PAML [30].

Akaike Information Criterion (AIC)
An AIC statistical test was invoked to determine the fit of
the two un-nested amino acid replacement matrices TM
and ITT to the membrane spanning domain dataset. A sec-
ond-order AIC was implemented because of the small
ratio of sample size to free parameters in the phylogenetic
analysis (AICJ) [50,511:

AIC, = (-21+ 2K) + 2K(K + 1)

where I is the maximized log-likelihood, K is the number
of free parameters (25 free-parameter branch estimates
plus one free-parameter for the gamma distribution), and
n is the sequence length from the multiple sequence align-
ment (253).

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BMC Genomics 2006, 7:44

Phylogenetic analyses
The CFTR tree topology searches were conducted using the
minimum evolution criterion with 10,000 replicates of
random sequence addition in PAUP [29]. Bootstrap anal-
ysis consisted of a fast-heuristic search of 1000 replicates
with re-sampling.

The ABC full-length and membrane-spanning domain-
only datasets were analyzed as above, with the exception
of 10 and 100 replicates, respectively.

The resulting topologies estimated in PAUP were subse-
quently used for maximum likelihood analyses in PAML
(v3.14) [30]. These included incorporating different
amino acid replacement matrices (Jones-Taylor-Thornton
and the estimated TM), calculating site-specific amino
acid replacement rates using posterior mean rates, and
estimating ancestral amino acid character states. All anal-
yses accounted for site-specific rate variation using a dis-
crete gamma distribution with eight rate categories.

SIFT analyses
We defined the homologous CFTR sequences, and their
subsequent alignment, for the SIFT analysis of the mem-
brane-spanning domains. This analysis did not exploit
SIFT'S ability to perform database searches to identify
homologous sequences. In this way, identical datasets
were used in the SIFT and DETECTER Dl analyses.

To extract information from a PAML output (rst files) pre-
senting the posterior probabilities of ancestral character
states throughout the evolutionary history of a protein
family, a Perl script was developed and is freely available
for download from our server [52].

Cystic fibrosis mutations
All mutations of the cystic fibrosis gene were downloaded
from the Cystic Fibrosis Mutation Database [18]. Mis-
sense mutations residing within the 12 transmembrane
helices were extracted.

Authors' contributions
FAG conceived and developed the DETECTER tool, and
prepared the manuscript. DWD developed computational
tools. SAB prepared the manuscript.

We thank Julian Zielenski and Lap-Chee Tsui at the Hospital for Sick Chil-
dren (Toronto, Canada), Ross Davis (Foundation for Applied Molecular
Evolution), and the insightful comments of three anonymous referees for
their assistance with our research and manuscript. This work was funded
by a NASA Astrobiology grant and NIH grant HG63368.

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