Evolutionary sequence modeling for discovery of peptide hormones.

Abstract

There are currently a large number of "orphan" G-protein-coupled receptors (GPCRs) whose endogenous ligands (peptide hormones) are unknown. Identification of these peptide hormones is a difficult and important problem. We describe a computational framework that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure across species and show how such models can be used to discover new functional molecules, in particular peptide hormones, via cross-genomic sequence comparisons. The computational framework incorporates a priori high-level knowledge of structural and evolutionary constraints into a hierarchical grammar of evolutionary probabilistic models. This computational method was used for identifying novel prohormones and the processed peptide sites by producing sequence alignments across many species at the functional-element level. Experimental results with an initial implementation of the algorithm were used to identify potential prohormones by comparing the human and non-human proteins in the Swiss-Prot database of known annotated proteins. In this proof of concept, we identified 45 out of 54 prohormones with only 44 false positives. The comparison of known and hypothetical human and mouse proteins resulted in the identification of a novel putative prohormone with at least four potential neuropeptides. Finally, in order to validate the computational methodology, we present the basic molecular biological characterization of the novel putative peptide hormone, including its identification and regional localization in the brain. This species comparison, HMM-based computational approach succeeded in identifying a previously undiscovered neuropeptide from whole genome protein sequences. This novel putative peptide hormone is found in discreet brain regions as well as other organs. The success of this approach will have a great impact on our understanding of GPCRs and associated pathways and help to identify new targets for drug development.

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Evolutionary Sequence Modeling for Discovery of
Peptide Hormones
Kemal Sonmez1¤a, Naunihal T. Zaveri1¤b, Ilan A. Kerman2, Sharon Burke2, Charles R. Neal2¤c, Xinmin Xie3,
Stanley J. Watson2, Lawrence Toll1*
1SRI International, Menlo Park, California, United States of America, 2Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, Michigan,
United States of America, 3AfaSci, Burlingame, California, United States of America
Abstract
There are currently a large number of ‘‘orphan’’ G-protein-coupled receptors (GPCRs) whose endogenous ligands (peptide
hormones) are unknown. Identification of these peptide hormones is a difficult and important problem. We describe a
computational framework that models spatial structure along the genomic sequence simultaneously with the temporal
evolutionary path structure across species and show how such models can be used to discover new functional molecules, in
particular peptide hormones, via cross-genomic sequence comparisons. The computational framework incorporates a priori
high-level knowledge of structural and evolutionary constraints into a hierarchical grammar of evolutionary probabilistic
models. This computational method was used for identifying novel prohormones and the processed peptide sites by
producing sequence alignments across many species at the functional-element level. Experimental results with an initial
implementation of the algorithm were used to identify potential prohormones by comparing the human and non-human
proteins in the Swiss-Prot database of known annotated proteins. In this proof of concept, we identified 45 out of 54
prohormones with only 44 false positives. The comparison of known and hypothetical human and mouse proteins resulted
in the identification of a novel putative prohormone with at least four potential neuropeptides. Finally, in order to validate
the computational methodology, we present the basic molecular biological characterization of the novel putative peptide
hormone, including its identification and regional localization in the brain. This species comparison, HMM-based
computational approach succeeded in identifying a previously undiscovered neuropeptide from whole genome protein
sequences. This novel putative peptide hormone is found in discreet brain regions as well as other organs. The success of
this approach will have a great impact on our understanding of GPCRs and associated pathways and help to identify new
targets for drug development.
Citation: Sonmez K, Zaveri NT, Kerman IA, Burke S, Neal CR, et al. (2009) Evolutionary Sequence Modeling for Discovery of Peptide Hormones. PLoS Comput
Biol 5(1): e1000258. doi:10.1371/journal.pcbi.1000258
Editor: Rama Ranganathan, UT Southwestern Medical Center, United States of America
Received June 9, 2008; Accepted November 21, 2008; Published January 9, 2009
Copyright: ? 2009 Sonmez et al. 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 author and source are credited.
Funding: This work was funded by grants R21DA016629 and R43MH072162.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: lawrence.toll@sri.com
¤a Current address: Departments of Computer Science and Electrical Engineering, Oregon Health and Science University, Beaverton, Oregon, United States of
America
¤b Current address: Xenoport, Santa Clara, California, United States of America
¤c Current address: Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, United States of America
Introduction
G protein coupled receptors (GPCRs) probably represent the
largest gene family, making up 3% of the mammalian genome [1].
These proteins are made up of several subfamilies, including Class
A rhodopsin-like, Class B secretin-like, Class C metabotropic
glutamate/pheromone-like, and other nonmammalian receptors.
Within each class, there is a very large number of smaller
subclassifications, such as a family of receptors for peptide
hormones within rhodopsin-like receptors. There are approxi-
mately 1,000 GPCRs, the vast majority of which are olfactory
receptors, with more than 650 GPCRs in the rhodopsin family
alone [2]. A large number of these receptors have been identified
only by computational methods, while others have been cloned
and transfected into cells; however, the cognate neurotransmitter
and the receptor functions for many GPCRs are currently
unknown. Any receptor for which the native neurotransmitter is
unknown is considered an orphan receptor. Of all the orphan
receptors that remain, some percentage represents receptors for
peptide hormones.
This large family of proteins is important not only from a basic
science perspective, but because of their extracellular sites of action
and importance as first messengers for cellular signaling, GPCRs
have become a primary target for drug development. In fact, over
30% of all pharmaceuticals act either as agonists or antagonists of
GPCRs [3]. Many pharmaceutical companies are identifying,
cloning, and patenting new orphan GPCRs, with the hope that
orphan receptors will ultimately lead to new drug development
and new pharmaceutical agents.
Although the identification of putative GPCRs can be
accomplished relatively easily, the discovery of the endogenous
ligands that activate these receptors is far more difficult. These
ligands can exist as small molecules, lipids, peptides, or proteins
[4,5]. Many, such as ATP, may have important functions other
than activating a GPCR. Even within a class of hormones, there
are seldom obvious clues that identify a new candidate. This is
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particularly true within the family of peptide hormones, as they are
processed from a larger species known as preprohormones [6].
Peptide hormones, or neuropeptides, are a string of amino acids
ranging from approximately 3 to 50 residues. They are found
within a larger protein (a preprohormone), and the production of
the actual hormone usually follows specific rules. Preprohormones
are secreted proteins, and each has a signal sequence that is
necessary for the transport of the protein out of the Golgi complex
into a secretory vesicle for processing and secretion where the
signal sequence is removed, revealing the prohormone [7]. In
general, hormones are surrounded by a pair of basic residues, i.e.
Arg-Arg, Arg-Lys, Lys-Arg, or Lys-Lys, which are found directly
adjacent to the putative hormone. These double basic residues act
as recognition sites for processing enzymes, usually serine proteases
that cleave the prohormone to liberate the active peptide [7,8]. In
many cases, there is more than a single active peptide within one
precursor protein [6].
Even with these common features, the identification of a peptide
hormone from a DNA or protein sequence is very difficult. Even
though all of the GPCRs are obviously related based upon DNA
or protein sequence, the neuropeptides that bind to the receptors
are only obviously related within discrete families of prohormones.
For instance, the family of opioid-like peptides has four members.
These prohormones, proopiomelanocortin (POMC), proenkepha-
lin, prodynorphin, and pronociceptin (proN/OFQ), share similar
genomic structures and a very slight similarity of protein sequence,
most notably the Y(F)GGF of enkephalin, b-endorphin, dynor-
phin, and N/OFQ [9,10]. However, if one were to conduct a
BLAST search in Genbank for DNA sequences similar to
proenkephalin, one would not find any other neuropeptide.
Simple search strategies within Genbank are not adequate for
identifying novel neuropeptides, especially those not belonging to
known neuropepeptide families.
There is an additional feature of neuropeptides that may more
clearly differentiate them from other types of molecules.
Neuropeptides are usually well conserved among various species
(rat, mouse, human), while the intervening sequences, presumably
because they are simply discarded, are not well conserved [11].
Here we describe a novel Hidden Markov Model (HMM)-based
computational framework, the Match Profile HMM (MPHMM)
method for neuropeptide identification based upon an approach
that models spatial structure along the genomic sequence
simultaneously with the temporal evolutionary path structure across
species, and show how such models can be used to discover new
functional molecules via cross-genomic sequence comparisons.
This computational tool was used to identify a novel prohormone,
NPQ, containing up to four potential neuropeptides [12]
Results
Computational Modeling of Preprohormone Evolution
by a Hierarchical Grammar of Evolutionary Probabilistic
Models
Hierarchicalgrammars
Hierarchical grammars of evolutionary HMMs, such as phylo-
HMMs or MPHMMs are probabilistic models that take into account
the way substitutions take place in the evolutionary path at specific
sites along the genome, and the specific patterns of change from one
site to the next. Figure 1 shows a hierarchical grammar of
evolutionary HMM modules for a preprohormone. At the
functional-level hierarchy, the model is specified in terms of its
functional elements, which are signal sequences, cleavage sites, and
preserved and diverged regions. The underlying evolutionary HMM
modules carry out the local multiple alignments with respect to the
phylogenetic relationship warranted by the context. This kind of
hierarchical alignment is significantly more informative than a
conventional multiple sequence alignment in that it provides a
segmentation that has to satisfyhigher-level constraints. For example,
for the peptide hormone problem, the most important feature of a
cross-genome alignment turns out to be the difference between the
substitution ratesof thefunctional
subsequences around (predominantly double basic residue) splicing
sites.
There are several formalisms for describing probabilistic
evolutionary algorithms in the literature. We follow the exposition
[13] used for the phylo-HMMs. Let us define the computational
structure of a hierarchical grammar of functional-evolutionary
model modules (MPHMMs or phylo-HMMs) by the four-tuple
H~ P,G,a,b
ð
component states (for functions such as a signal sequence, a
splicing site, or a peptide) with the set of associated functional
element models, G~ G1,:::,GM
f
for the part of the sequence alignment at the component state pj.
a~ ajk
ð
component state transition probabilities and the vector of initial
probabilities, respectively. In this formulation, for the sake of
descriptive efficiency, we are describing the basic two-level
hierarchy of models, which can, in our implementation, entail
more levels. In the lower level of the hierarchy, each component
model is a vector output HMM with an alphabet consisting of the
four-tuple, Gj~ Sj,Mj,Aj,bj
with the functional component module. For example, a simple
double basic residue cleavage site HMM would have two states
that emanate multiple alignments of Arg and Lys residues. The set
of associated functional element models, Mj~ Mj
account for the amino acid sequence with Ajand bjas the matrix
of lower level state transition probabilities and the vector of initial
probabilities, respectively. This structure also supports hierarchical
grammars of phylo-HMMs [13]. In that case, Gj~ Qj,pj,tj,nj
where Qjis the substitution matrix defined with respect to the
alphabet of amino acids, pjis a vector of equilibrium frequencies,
ofMPHMM modules.
and thenonfunctional
Þ, where P~ p1,p2,:::,pn
fg is a set of functional
g, with the model Gjaccounting
??, 1ƒj,kƒM
Þ, and b~ b1,:::bM
fg are the matrix of
??, where Sjis a set of states associated
1,:::,Mj
m
no
??,
Author Summary
Peptide hormones, or neuropeptides, are made up of a
string of amino acids ranging from approximately 3 to 50
residues.Thesepeptidesareprocessedfromalargerprotein
called a prohormone and activate a class of proteins called
G-protein-coupled receptors (GPCRs). Neuropeptides signal
neurons and other cells leading to changes in cellular
biochemistry and potentially gene expression. There are a
number of ‘‘orphan’’ GPCRs, i.e., receptors that have been
discovered either by genomic sequence or by cloning, in
which its respective peptide hormone is unknown. We have
devised a computational method that models patterns in
protein sequence simultaneously with evolutionary differ-
ences across species in order to identify previously
unknown peptide hormones. We have used this computa-
tional methodology to identify a previously unknown
putative prohormone that contains up to four potential
neuropeptides, and we havecharacterizedthis prohormone
with respect to location in rat brain and various human
tissues. This computational technique will be useful for the
identification of additional neuropeptides and help to
characterize orphan GPCRs. Because roughly half of all
pharmaceuticals act through activation or inhibition of
GPCRs, this technique should lead to the identification of
additional pharmaceutical targets and ultimately clinically
used drugs.
Computational Neuropeptide Discovery
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tjis the binary phylogenetic tree with the set of branch lengths nj.
For phylo-HMMs, Felsenstein’s ‘‘pruning’’ algorithm [14] is used
for the phylogenetic model optimization rather than Viterbi for
our model.
In this two-level hierarchical approach, there are two types of
alignments,(i)functional alignments
C~ C1,:::,CL
ð
level, Xk~ Xk
Lk
, k~1,:::,L. To illustrate this point,
Figure 2 shows a hierarchical alignment for prepronociceptin from
five species (human, chimp, mouse, rat, cow), where the boxes
depict the functional element sequence. The resulting sequence
alignments within functional elements are also shown.
A path through the functional element sequence is a sequence of
states w~(w1,:::,wL), and a path through a component module is a
sequence of states t~ t1,:::,tL
ð
at thehigh level,
Þ, and (ii) state module alignments at the lower
1,:::,Xk
??
Þ. Given the above setting, we
compute the joint probability of a functional level path and
alignment, which is given by
P w,C H
ð
j
Þ~bw1P C1Gw1
??
??
P
i~2awi{1wiP CiGwi
L
??
??,
where, in turn, each of the functional module state alignments is
given by
P t,XjGj
??
??~bj
t1P Xj
1lj
t1
??
??
P
L
i~2aj
Þ~P
ð
ti{1tiP Xj
ilj
ti
??
??
:
The likelihood of the model P C H
summing over all possible paths, and the maximum likelihood
path is the path that maximizes P w,C H
j
ð
wP w,C H
ð
j
Þ is found by
jÞ. The computation of
Figure 1. Prohormone hierarchical grammar of evolutionary MPHMM modules.
doi:10.1371/journal.pcbi.1000258.g001
Figure 2. Hierarchical functional-element multiple alignment of Pronociceptin across human, chimpanzee, mouse, rat, and cow.
doi:10.1371/journal.pcbi.1000258.g002
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these quantities and the state posterior probabilities are facilitated
by the Markovian structure that allows standard dynamic
programming based solutions through the use of Viterbi and
forward-backward algorithms.
Component MPHMM modules.
the structural constraints of a preprohormone sequence by
modeling separate modules in a combined manner by a modular
profile HMM for each genome. The two modular HMMs for the
two genomes are then coupled by several pairwise HMMs on a
module-by-module basis across the two genomes in order to model
differential evolutionary rates of functional and nonfunctional
sequences.Wename the overall
Grammar of Hmms of Evolutionary Regions (HIGHER).
The structural topology of the modules comprises a signal
sequence module, nonfunctional preprohormone module, splicing
site module, and the functional hormone module in various
possible combinations. Specifically, the signal sequence HMM
module is shown in Figure 3a. This is essentially a topology similar
to the HMM topology used by Nielsen et al. [15,16], which models
a general signal sequence with the requisite sites and results in a
similar detection performance as that of SignalP [17].
There are two possible topologies for splicing site modules, as
shown in Figure 3b, for two adjacent basic residues, and for a
single basic residue [18]. Two consecutive basic residues is the
simplest splicing site model, consisting strictly of two K or R
residues in sequence, and is sufficient for the majority of known
peptides. A single basic residue splice site occurs for an important
number of peptides, though, and the model shown in Figure 3b, in
which a single residue of K or R occurs in a context with a specific
MPHMMs account for
frameworkHierarchical
residue profile, can be trained with synthetic data generated based
on sequences published by Devi [18].
The relative homology between hypothesized peptide hormone
and divergent sections is modeled through the use of pairwise-
HMMs, or their straightforward generalizations to multiple
alignments of N sequences in which all 2Nsubsets occupy a
separate state. Figure 3c depicts the structure of the pairwise-
HMM for aligning two sequences. The relative difference between
the homologies of the hypothesized and divergent regions
produces the most informative feature from the alignment of
multiple sequences to determine if the aligned sequences constitute
a preprohormone by satisfying both the structural and the
evolutionary constraints.
Computational processing
statistical models that make extensive use of graphs, such as
phylo-HMMs and HIGHER, are usually quite costly to compute.
Estimation of models based on alignments of a multitude of
genomes (more than five for example) requires considerable
resources in terms of both CPU power and data. This fact limits
their applicability as general filters or data mining tools that
operate on large repositories of sequences for discovery. In our
processing of all the protein sequences that were available to us, we
had to address this difficulty, for which we used the hierarchical
structure to our advantage by first forming initial raw alignments
based on parsing of sequences with our functional element
grammar, and aligning based on functional element identity alone.
Then, the resulting sequences were realigned by the HIGHER
model in order to obtain the fine alignment as well as the
discriminatory features. More specifically, processing of the
steps.
Two-dimensional
Figure 3. HIGHER MPHMM modules. (a) Signal sequence, (b) cleavage site, and (c) peptide/divergent region modules.
doi:10.1371/journal.pcbi.1000258.g003
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sequences followed three main steps: two multiple alignment steps;
(i) raw multiple alignment via functional element detection, and (ii)
fine multiple alignment via fitting of the MPHMM, and (iii) a final
discrimination step where a score is generated from the multiple
alignment. After the sequences were processed and scored,
alignments were generated, and the biologists were provided
with the list of hits in a graphical user interface. This interface was
used to browse the list of hits with a more discriminatory viewing
tool that includes constraints to filter the list of hits, e.g. according
to region, lengths or maximum divergence.
Summary of processing steps
1. Functional element transcription of protein sequences
from several genomes using the detector HMM modules and
the preprohormone grammar. See Table 1 for modules and
their abbreviations.
Example alignment :
startzSSzDRzCSdzPRzCSszDRzend
2. Multiple functional element alignment of protein
sequences (Figure 4)
3. Fit HIGHER model to the multiple sequence alignment
4. Browse the matches via the user interface Sequence-
Matcher in the feature space to evaluate the hits (see http://
www.cslu.ogi.edu/people/sonmezk/hormone).
Availability of Human-Mouse Search Results,
SequenceMatcher, and the HIGHER Tools
The extended list of matches, the GUI SequenceMatcher, and
the HIGHER tools will be made are available at http://www.cslu.
ogi.edu/people/sonmezk/hormone. Initially, we will enable the
visualization of our ENSEMBL and CELERA runs via the GUI.
The next version will allow evolutionary HMM searches specified
by the user. The HIGHER codebase will also be made available at
the website once it is ready for release.
Search of SwissProt Database.
presentresultsonSwissProt41,adatabase containinga largenumber
of known hormones. Because the functions of all of the proteins in
SwissProt are known, this search does not produce novel peptide
hormones, but it produces a detection metric for the performance of
the search paradigm. Note that the structural profile HMMs for the
signal sequence and the splicing sites have not been trained with these
proteins,and in HIGHER we do not trainsequence structuremodels
for hormones, so ourSwissProt set constitutesan independent test set.
For one specific threshold, we were able to identify 45 out of 54
preprohormones known to be in SwissProt with 44 false alarms
(Table 2). In terms of detection performance, this corresponds to a
point on the receiver operating characteristic (ROC) curve with
sensitivity of 83%,and specificityof more than 99.9%(44 false hitson
a SwissProt set with 122,564 proteins).
Search of the Celera Database.
list of known and putative proteins from mouse and human
genomes using the Celera Discovery System (CDS) database.
These two sets of proteins were matched using HIGHER and the
resulting output examined for known and potentially novel peptide
hormones. Each potential match was examined using the CDS
that lists families to which these unknown proteins might belong.
BLAST searches were also conducted on both the predicted
protein and the mouse and human gene, using both CDS and
Genbank. A gene family was evident for many of the potential
matches, suggesting that these proteins did not represent novel
neuropeptides. For a smaller number of matches, the function of
As a proof of principle, we
We then collected the full
Figure 4. Multiple alignment of functional element sequences across genomes.
doi:10.1371/journal.pcbi.1000258.g004
Table 1. Modules and their abbreviations.
Functional Element Symbol
Signal sequence SS
Cleavage site (double basic) CSd
Cleavage site (single basic) CSs
Peptide hormone region PR
Divergent regionDR
doi:10.1371/journal.pcbi.1000258.t001
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the protein was unknown. We consider these to be potential novel
preprohormones.
One novel protein identified is the perfect example of our
hypothetic neuropeptide model, shown in Figure 5. Between
double basic residues, the homology is high. Outside these residues
the conservation is quite low. The protein sequence of the human
and rat were predicted from gene finding programs. These
proteins have no apparent homology to any other proteins, and no
known biological function. Of the four potential neuropeptides
(highlighted in yellow, beginning at the end of the signal sequence
and ending at the fourth set of basic residues), the most likely
candidate would be the NPQ peptide NWTPQAMLYLKGAQ-
NH2, although we should emphasize that one or more of the
others (APQRLLE, FISDQS, and KDLSDRPLPE) are also likely
to have biological activity. This amidated 14 amino acid peptide
(we expect the G before the RR to be a substrate for the amidating
enzyme peptidylglycine a-amidating monooxygenase, PAM [19])
is fully conserved among human and mouse. A further search of
homologies for this protein found strong conservation for the
amidated 14 amino acid peptide as far back as fugu. The fact that
this portion of the protein is so highly conserved, including
amidation and processing sites, strongly suggests the importance of
this peptide sequence.
One interesting mutation in the rat gene is not found in the
human, mouse, bovine, porcine or fugu protein. The rat protein
has a mutation in the GRR at the C-terminal portion of the NPQ
peptide. A single nucleotide change produces the sequence Gly-
His-Arg. This complicates the processing of the rat gene product.
It is possible that an endopeptidase may function at a His-Arg
bond, and if so, it would become a substrate for carboxypeptidase
E (CPE) [20], and the processed peptide would end Gln-Gly-His,
without the amidation of the more abundant analog.
ESTs for preproNPQ have very recently appeared in GenBank
indicating that the human protein can be found in brain, ovary,
kidney and lung cancer cells. Our preliminary investigation of
preproNPQ using RT-PCR shows the presence of its transcripts in
Table 2. Matches Found in Swiss-Prot Database.
Hormones
Sequence
Matching ‘‘Hits’’Hormones
Sequence
Matching ‘‘Hits’’
ACTHx MCH (melanin concentrating hormone)
ADM (adrenalmedulin)xMotilinx
Agouti-related peptidesx MSH (melanocyte stimulating hormone)x
Amylin xNeuromedin Ux
ANP (atrial natruretic peptide)Neurotensinx
Apelin Neurturinx
Calcitoninx Nociceptinx
CART (cocaine and amphetamine regulated
transcript)
xNPY (neuropeptide Y) x
CCK (cholecystokinin)xOrexins x
CGRP (calcitonin gene related protein)x Oxytocinx
CNP (C-type natriuretic factor)xPACAP (pituitary adenylate cyclase activating
polypeptide)
x
Cortistatin PPY (pancreatic hormone)x
CRF (corticotropin releasing factor)x PHI (same precursor with VIP)x
Dynorphinx PrRP (prolactin-releasing peptide)
b-EndorphinxPTH (parathyroid hormone)x
Endothelin 1xPTH-RP (parathyroid releasing hormone)
Endothelin 2xPYY (peptide YY) x
Endothelin 3xSecretinx
Enkephalin xSomatostatin
Galanin xSubstance K (= neurokinin A)
Gastrinx Substance Px
Glucagonx TEGT (testis enhanced gene transcript)x
GRF (growth hormone releasing factor)xTRH (thyroid releasing hormone)x
GRP (gastrin releasing peptide)Vasopressinx
GuanylinVIP (vasoactive intestinal peptide)x
LHRH1 (luetinizing hormone releasing hormone)xPSP94 (prostate secretory protein)x
False Positives
Other signaling molecules: FGF-3,5,7,10,17,18; GDNF; CD8,28; PDGF-2; TGF; VEGF (vascular endothelial growth factor); HBNF-1; MIP; NGF (nerve growth factor); Cytokine
A21, IFN-a (interferon alpha); IGF binding protein 1B,2,3; IL7 (interleukin 7).
Other: MAGF (microfibril associated protein), MINK (K-channel), K-channel related peptide, L-type Ca2+channel, gamma subunit, myelin Po protein, Dif-2, Eosinophil,
Syntaxin 1B (vesicle docking), Syntaxin 2, TMP21 (vesicle trafficking protein), Coagulation factor III, PGD2 synthase, syndecans, FKBP12 (FK506 binding protein), Folate
receptor, ERp29, COMT, Connexin 32, Cytostatin.
doi:10.1371/journal.pcbi.1000258.t002
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human, mouse, and rat brains (Data not shown). We have cloned
and sequenced the human, mouse, and rat cDNAs, and have
verified the single nucleotide change that leads to the GHR
sequence in the rat preproNPQ gene. Northern analysis using a
human tissue blot (Clontech) showed the presence of preproNPQ
mRNA in brain and pancreas, but most prominently in the kidney
(Figure 6). Therefore, NPQ may be one of many peptides (such as
vasopressin) found in both brain and kidney.
We have also conducted studies to determine regional
localization in brain by in situ hybridization (Figure 7). An initial
mapping study of preproNPQ mRNA demonstrated that its
expression in the brain is restricted to the mesopontine
tegmentum. At its caudal extent preproNPQ mRNA is confined
to the Barrington’s nucleus, which can be identified by its
expression of corticotrophin releasing factor (CRF) mRNA
(Figure 7B, arrow). As illustrated in Figure 7A–C, regional
distribution of preproNPQ mRNA overlaps closely with that of
CRF, suggesting possible cellular co-localization of these two
mRNAs. In contrast, preproNPQ signal is distinct from that of
tyrosine hydroxylase (TH) (Figure 7D–F), which is selectively
expressed in locus coeruleus (Figure 7E, arrow). PreproNPQ
mRNA is also closely related to, but does not overlap with, choline
acetyltransferase (ChAT) (Figure 7G–I), which is expressed within
the laterodorsal tegmental nucleus (Figure 7H, arrow). At this level
of the neuraxis preproNPQ mRNA is located quite a bit lateral to
the majority of the midline serotonergic neurons, as determined by
examination of mRNA distribution of the synthetic enzyme
tryptophan hydroxylase 2 (TPH2) (Figure 7J–L), though there is
some overlap with the laterally displaced TPH2-positive neurons
(Figure 7L).
At the level of the caudal periaqueductal gray (PAG),
preproNPQ mRNA expression is restricted to the ventrolateral
quadrant of this structure (Figure 8) with some scattered signal in
the underlying reticular formation. Caudal ventrolateral PAG is a
heterogeneous brain region that contains dopaminergic, choliner-
gic and serotonergic neurons. To determine whether preproNPQ
mRNA signal overlaps with any of these populations, in situ
hybridization (ISHs) for TH, ChAT and TPH2 were carried out.
ISH for TH showed a weak but specific signal within the
ventrolateral PAG (Figure 8B, arrow) that overlapped with
preproNPQ signal (Figure 8A–C). ChAT mRNA was closely
related to the preproNPQ signal but did not appear to overlap
with it (Figure 8D–F). Likewise, laterally-displaced TPH2 mRNA
was in close proximity to preproNPQ mRNA (Figure 8G–I).
Discussion
Because devising computer-generated methods of identifying
peptide hormones has been difficult, biochemical methods have
been the most relied upon. Although they are time consuming and
expensive, these methods work if one has some preliminary
information or basic assumptions. Substance P was discovered
based upon the physiological actions of brain extracts [21], while
the peptide hormones met- and leu-enkephalin were discovered
based upon a preexisting receptor [22]. Hughes and Kosterlitz
used a smooth muscle bioassay for opiate receptors to isolate two
peptides from bovine brain that were subsequently found to bind
to the opiate receptors [22]. It was only several years later that
these two peptides were found to be generated from a single
prohormone [23]. Mutt and colleagues used a chemical assay to
identify carboxy terminal amidated peptides, and in this way
discovered neuropeptide Y (NPY) and peptide YY [24]. The
purification and sequencing of N/OFQ (formerly nociceptin/
Figure 5. Amino acid sequence of preproNPQ. Sequences shown were obtained from GenBank. The human and rat sequences were verified by
nucleotide sequencing as described in Materials and Methods. Putative neuropeptides highlighted. They begin at the end of the signal sequence and
end at the fourth set of basic residues. Residues that are not conserved between human and other species are in bold.
doi:10.1371/journal.pcbi.1000258.g005
Figure 6. Northern Blot Analysis of preproNPQ mRNA. Ambion’s
First Choice Human Blot was prehybridized and probed with human
NPQ cDNA prepared from the human DNA clone in pOTB7 vector from
ATCC (Cat # 6710068, Manassas, VA). This clone contained the putative
sequence for human NPQ. Random-prime labeling was performed
using
described in Materials and Methods. 1. Brain, 2. Placenta, 3. Skeletal
muscle, 4. Heart, 5. Kidney, 6. Pancreas, 7. Liver, 8. Lung, 9. Spleen, 10.
Colon.
doi:10.1371/journal.pcbi.1000258.g006
32P-dCTP and Klenow DNA polymerase was conducted as
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Page 8

orphanin FQ) was possible because of the availability of CHO cells
transfected with NOP (N/OFQ peptide) receptors (formerly called
ORL1) and the knowledge that the endogenous ligand would
inhibit cAMP accumulation, as do the endogenous ligands for m, d,
and k opioid receptors, the other receptors in that family [25,26].
Other examples of ‘‘reverse pharmacology’’ have followed, i.e.
[27], and each has led to great strides in the understanding of
human physiology.
Even though neuropeptides have very few apparent similarities
as a class, computational tools can be used to characterize and
even potentially identify new members of this class of signaling
molecules. Bakalkin and colleagues have examined the bioinfor-
matics of neuropeptides [28,29]; they have computed the amino
acid composition and relative amino acid arrangements in the
neuropeptide portion and compared them to the intervening
portions of a prohormone. Using this statistical method, they have
found an increased content of certain residues, as well as an
increased occurrence of certain pairs of residues, as compared to
proteins and non-regulatory peptides.
Although these biochemical and bioinformatics approaches can
provide useful information about neuropeptides and potentially
identify new neuropeptides, if the cognate receptor is unknown,
they will not be able to provide a general format for the
computational identification of this class of hormones. Such a
general format can be achieved using more sophisticated
computational tools such as Hidden Markov Models.
Hidden Markov models were originally developed for speech
recognition [30] and have long come to form the basis of the state
of the art in that field. Estimation and hypothesis testing
algorithms for HMMs have been well studied, and a wealth of
experience makes it possible to train and test large-scale models
from large amounts of data. Development of automatic speech
recognition systems has motivated one of the key aspects of the
presented approach in that there is a direct analogue between
using hierarchical sentence, word, and phone hidden Markov
models in speech recognition and the hierarchical modeling of
functional elements in this work. A prohormone may be viewed as
a sentence formed in a certain grammar using specific words, i.e.
functional elements, which in turn are modeled by a sequence of
phones, i.e. amino acids.
It is useful to differentiate between two usages of HMMs in
biological sequence analysis: (1) Pairwise-HMMs [31] are a
stochastic generalization of the sequence alignment algorithms
and may be regarded as probabilistic model based counterparts of
existing techniques, such as BLAST [32]. Their distinguishing
characteristic is that as models they generate alignments of two
sequences, their hidden states corresponding to insertions,
deletions or substitutions. (2) Profile HMMs [33,34] have proven
to be a major breakthrough in biological sequence analysis,
enabling modeling of protein families with a high degree of
Figure 7. In situ hybridization of preproNPQ mRNA. Expression
of preproNPQ mRNA in the rat brain at the level of Barrington’s nucleus
and locus coeruleus. In situ hybridizations (ISHs) for preproNeuropep-
tide Q (NPQ; A, D, G, and J), corticopin-releasing factor (CRF; B), tyrosine
hydroxylase (TH; E), choline acetyltransferase (ChAT; H), and tryptophan
hydroxylase 2 (TPH2; K) were carried out on adjacent 10 mm-thick
sections of the rat brain. ISH autoradiograms were digitized; images
were then inverted and pseudocolored according to the following
scheme: NPQ – green, CRF – red, TH – cyan, ChAT – white, and TPH2 –
blue. To determine whether NPQ signal overlapped with any of the
other signals, the sections were aligned and overlaid with each other (C,
F, I, L). Arrow in panel A indicates location of NPQ mRNA, while arrow in
panel B indicates location of CRF mRNA; note the mixing of red and
green (to yield yellow) in panel C (arrow) that suggests co-localization
of NPQ and CRF. Arrow in panel E indicates locus coeruleus and its TH-
positive neurons. Panel F shows that TH and NPQ signals are spatially
very close without overlap. Arrow in panel H indicates the cholinergic
laterodorsal tegmental nucleus, while panel I illustrates close spatial
relationship between ChAT and NPQ mRNAs. At this level of the
neuraxis there is little overlap between TPH2 mRNA (blue signal in
panel K, which represents serotonergic neurons) and NPQ (L).
doi:10.1371/journal.pcbi.1000258.g007
Figure 8. In situ hybridization of preproNPQ mRNA. Expression
of preproNPQ mRNA at the level of the caudal ventrolateral
periaqueductal gray (PAG). ISH autoradiograms were digitized and
pseudocolored according to the same scheme as in Figure 7. NPQ
signal was visible in the ventrolateral quadrant of the PAG as well as
within the underlying reticular formation (A, D, G). ISHs for TH (B), ChAT
(E) and TPH2 (H) were carried out on adjacent sections. Arrow in panel B
indicates location of dopaminergic TH-positive neurons of the
ventrolateral PAG that appear to overlap with a subset of NPQ mRNA
(C). There is also close spatial relationship between NPQ and ChAT (F)
and NPQ and TPH2 (I). Abbreviations are the same as in Figure 7.
doi:10.1371/journal.pcbi.1000258.g008
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functional accuracy. For over a decade they have formed the basis
of the most widely used applications of sequence modeling in
molecular biology [35]. Profile HMMs, as models, generate a
single sequence with a set of hidden states corresponding to the
genomic structure of the molecule.
The basic computational units in this work are Match-profile
HMMs (MPHMMs) [12,36], which combine the capabilities of the
two types of HMMs in that they can be viewed as using a profile
HMM structure in modeling the sequence structure and a pairwise
HMM (or a multiple-genome generalization thereof) in modeling
the evolutionary characteristics of variation across species. In
particular, the composite structure by which the preprohormone
evolution is modeled is a hierarchical grammar of MPHMMs.
Hierarchical grammars of MPHMMs are probabilistic models
that take into account the manner in which substitutions take place
in the evolutionary path at specific sites along the genome and the
specific patterns of change from one site to the next. This kind of
hierarchical alignment is significantly more informative than a
conventional multiple sequence alignment (e.g., a la ClustalW) in
that it provides segmentation of functional context. For example,
for the peptide hormone problem, the most important feature of a
cross-genome alignment turns out to be the difference between the
substitution rates of the functional and the nonfunctional
subsequences around (predominantly double basic residue)
splicing sites.
There are several approaches in the literature for addressing
similar problems. Phylogenetic HMMs, or phylo-HMMs, are
probabilistic models that combine HMMs and phylogenetic trees
in order to explain the spatial (genomic) and temporal (evolution-
ary) characteristics of a sequence, an excellent review of which is
provided by Siepel and Haussler [13]. The first introduction of
phylo-HMMs was motivated by the need to improve phylogenetic
models that allow for variation in the substitution rate across sites
[37,38]; subsequently the problem of secondary structure
prediction was addressed [39,40]. Recently there is increased
interest in these models as cross-genomic data become available in
large quantities, and approaches that are informed by evolutionary
pressures become enormously useful [41–45]. In particular, they
have been applied to cross-genome gene prediction [46,47].
Another similar structure is the evolutionary HMM [48,49] that
accounts for the phylogenetic information using generalizations of
pairwise-HMMs, in a way similar to our approach. Evolutionary
HMMs do not model the genomic structure in a targeted manner,
as we do through the use of hierarchical grammars, and the spatial
part of the model is used to track shifts in phylogenetic parameters.
Recently, a paper describing a different HMM-based method
for the genomic identification of neuropeptides was published
[50]. This paper used a single species method containing the
peptide features we describe here, including a signal sequence,
peptide, and prohormone cleavage site. The main difference of
our approach from the published work is the use of cross-species
comparisons through evolutionary models. In fact, there is prior
work on discovery based on genomic structure alone. The problem
of computational peptide hormone discovery based on genomic
structure alone proves to be difficult. For example, an attempt to
build models by specifying rules via deterministic grammars within
the inductive logic grammar framework is described by Muggleton
et al. [51]. In their manuscript, by enforcing the existence of signal
sequences and splicing sites through a deterministic context-free
grammar, a sieve for possible prohormone sequences is proposed.
Even without the insight provided by evolutionary forces, the
resulting method is able to eliminate unlikely candidates, but due
to the ubiquitous existence of double basic residues throughout
protein sequences, its selectivity turns out to be poor. In our
approach, it is because of the signature of stochastic evolutionary
pressures on the protein sequences that small functional peptide
islands can be identified in the midst of a sea of diverged
sequences.
In addition to our proof of principle using the Swiss Prot
database, we have identified a number of potential preprohor-
mones and their proposed processed neuropeptides. There were
many unknown secreted proteins identified directly from the
sequence matching protocol that fit the simple criterion of a pair of
basic residues surrounding 4–50 amino acids. Visual examination
of each possibility often detected reasons to decrease the likelihood
that a particular protein was in fact a prohormone. There were
three proteins for which we determined the presence of transcripts
in the brain, and only one that was further characterized. Because
preproNPQ, contains four potential biologically active agents, and
because one of these was amidated, we considered this our most
likely prohormone, with the most likely neurpeptide being the 14
amino acid amidated NPQ peptide (Figure 5). This peptide is
conserved in mouse, dog, cow, and human sequences. It is
conserved, except for a single amino acid change, as far back as
fugu. The mRNA coding for this protein is found in brain, with
higher levels in kidney.
Anatomical localization of preproNPQ mRNA with ISH
demonstrated that its distribution is restricted to a very specific
site in the brain (Figures 7 and 8). Our studies indicate that
preproNPQ-containing cells overlap in their distribution with cells
that express CRF in Barrington’s nucleus, as well as those that are
serotonergic and dopaminergic in the ventrolateral PAG. These
results raise the possibility that preproNPQ may be co-localized in
the same neurons with these neurotransmitters. Furthermore, this
peptide is distributed closely to the cholinergic neurons of the
mesopontine tegmentum raising the possibility that NPQ peptides
may also interact with the cholinergic system.
The functional significance of these findings will require
additional behavioral and physiological investigations, but it is
reasonable to speculate that NPQ peptides may be involved in
regulating a number of diverse functions. These likely include
regulation of urinary, gastrointestinal and general autonomic
functions, since Barrington’s nucleus contains neurons that send
polysynaptic projections to the bladder, colon, spleen and kidney
[52,53]. CRF-containing neurons in the Barrington’s nucleus have
been proposed to play a role in mediating stress-induced colonic
alterations [54]. Based on the close overlap between CRF and
preproNPQ, it seems feasible that NPQ peptides may play a role
in the pathophysiology of stress-induced gastrointestinal distur-
bances.
Along the same lines of modulation of stress responses,
dopaminergic (TH-positive) neurons of the ventrolateral PAG
have been shown to project to the CRF-containing area of the bed
nucleus of the stria terminalis (BNST) [55,56], where these
projections have been proposed to modulate CRF-initiated startle
response [55]. Since we found close overlap between preproNPQ
ISH signal and that for TH in the ventrolateral PAG, it is tempting
to speculate that one of the NPQ peptides may play a role in
regulating CRF-induced stress responses. The biological activity of
the NPQ peptides is now under investigation.
Using a different HMM-based method for the genomic
identification of neuropeptides, Mirabeau et al. identified two
putative prohormones and processed peptides [50]. One peptide
that was termed Spexin is identical to NPQ. They found spexin to
co-localize with insulin in secretory granules, when transfected into
rat pancreatic cells. ISH studies detected spexin mRNA only in the
submucosal layer of the esophagus and stomach. Spexin mRNA
was not reported in the brain. Finally, they showed that the
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amidated 14- amino acid peptide induced contractions of the rat
fundus muscle of the stomach. This is an interesting observation,
since our findings indicate that the rat almost certainly does not
make amidated peptide because of the single amino acid change
found within the C-terminal cleavage site (see Figure 5).
Demonstration of functional activity of this compound in the rat
stomach suggests that the C-terminal portion of NPQ is likely not
involved in binding to its still unidentified receptor.
The computational method that led to the discovery Spexin
identified another peptide that the authors named augurin [50].
Augurin is an uncharacteristically long peptide, 78 amino acids
within a prohormone of length 148 amino acids. In terms of
scoring, we are heavily penalizing peptides that are long with
respect to their flanking non-functional sequences, and in the
viewer, we have a filter that eliminates altogether any hit with
length greater than 50% of the whole protein length. An
experiment that modified our scoring and filters to test whether
our model also works for augurin, verified that augurin was indeed
detected by HIGHER as an instantiation of the following structure
startzSSzDRzCSdzPRzend
in our grammar.
There are other neuropeptides, which were not identified using
our MP-HMM techniques. There are several potential reasons for
other missing neuropeptides, the first and probably most
important of which relates to the dataset used. The datasets of
known and hypothetical proteins do not contain all the
preprohormones. Although the genomes of mouse and human
have been sequenced, the complement of predicted proteins is
constantly changing and is different in the different databases.
Another reason for not identifying prohormones is that the MP-
HMM methodology utilized is statistical in nature and will not
necessarily identify 100% of the target proteins. There are also
many prohormones that do not have the classical profile of pairs of
basic residues surrounding the neuropeptide. We are currently
implementing a single basic residue algorithm based upon known
splicing characteristics [18] that should lead to the identification of
additional neuropeptides.
Conclusion
We have presented a computational framework that is capable
of accounting for protein structure and cross-species evolutionary
divergence simultaneously. By aligning low-level evolutionary
HMM modules within a high-level functional-element grammar, it
is possible to build precise models of the effects of evolutionary
pressures on genomic structures. In particular, we have applied
this technique to modeling of prohormones across species with the
goal of identifying novel prohormones and associated peptide
hormones based on their evolutionary divergence profiles and
genomic structures. This technique has resulted in high accuracy
detection in a known dataset and led to putative hormones in a set
of hypothetical proteins. Biochemical validation of the findings has
resulted in the initial characterization of the prohormone
preproNPQ, containing four potential previously undiscovered
neuropeptides.
Materials and Methods
Polymerase Chain Reaction (PCR) of cDNA from Brain
Using Species-Specific Primers
In order to determine if the putative transcript named preproneur-
opeptide Q (preproNPQ) is found in the brain, we performed PCR
using rat, human and mouse specific primers with their correspond-
ing cDNAs. The sequences of the primers used were: Rat Forward
Primer59-GAAGGGGCCGAGCATCCTGG-39
Primer 59-CACCAGTAAAAGCGTCTGTCTTC-39; Mouse For-
ward Primer 59-GGACAGGGTCGGAACATGAAG-39 and Re-
verse Primer 59-GTGTTTTCACCAGTTGAAGAGTC-39; Hu-
man Forward Primer 59-ACGCAGAACATGAAGGGACTCAGA-
39
andReverse Primer59-CCAGTATATTTTCACCAGT-
TAAGC-39. Advantage Genomic Polymerase Mix enzyme (BD
Biosciences Clontech, CA) was used for PCR, according to
manufacturer’s instructions. Approximately 200–300 ng cDNA was
used for each 50 ml reaction, along with 10 mM of specific forward
and reverse primer, 2.2 ml magnesium acetate and dNTPs (10 mM).
The annealing temperature was set at 53uC, and after 25 cycles of
amplification, the PCR products were run on a 1.5% agarose gel and
visualized using ethidium bromide. A positive control PCR reaction
was also performed at the same time, using rat brain cDNA and
specific primers for the prepronociceptin gene, and the reaction
product was run on the gel.
and Reverse
In Situ Hybridization in Rat Brain Slices Using Rat NPQ
Probe
Tissue collection.
Rats
decapitation using a guillotine. The brains were extracted, flash
frozen in 2-methylbutane at 230uC, and stored at 280uC. Brains
from each animal were cryostat sectioned coronally to a thickness
of 10 mm at 220uC and thaw-mounted onto Superfrost slides
(Fischer Scientific, Pittsburgh, PA). Slides were collected in sets of
10, and adjacent sections were placed on consecutive slides. This
strategy allowed us to perform in situ hybridization (ISH) for
different mRNAs on adjacent sections. The radioactive signal from
these adjacent sections was digitally overlaid to determine regional
localization of preproNPQ mRNA.
In situ hybridization (ISH).
280uC and placed in 4% paraformaldehyde at room temperature
for 1 hour. Slides were washed 3 times in 26 SSC (300 mM
NaCl/30 mM sodium citrate, pH 7.2) for 5 min, washed in 0.1 M
TEA with 0.25% (vol/vol) acetic anhydride (pH 8.0) for 10 min,
dehydrated through a series of alcohol washes (50%, 75%, 90%,
95% 62, 100% 62 EtOH, for 30 seconds each), and air dried.
Radioactive probes for preproNPQ, tyrosine hydroxylase (TH;
synthetic enzyme for dopamine and norepinephrine), tryptophan
hydroxylase 2 (TPH2; synthetic enzyme for serotonin), choline
acetyltransferase (ChAT; synthetic enzyme for acetylcholine), and
corticotropin-releasing factor (CRF) were prepared from E. coli
containing pBluescript SK cloning vectors (Stratagene, San Diego,
CA), which were grown at 37uC for 16 hours in a shaker. The
preproNPQ probe was designed to be 340 nucleotides in length.
Those for TH, TPH2, ChAT and CRF were 274, 1030, 520, 762
nucleotides in length, respectively, and were based on publicly-
available sequences downloaded from NCBI Entrez Gene (http://
www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=
gene).
To verify that the inserts were of predicted lengths, DNA was
extracted and the inserts were excised with appropriate restriction
enzymes. The products were separated by gel electrophoresis on a
2% agarose gel and visualized with ethidium bromide. Probes
were also sequenced using dideoxynucleotide sequencing at the
University of Michigan’s DNA Sequencing Core. Sequenced
products showed perfect alignment with predicted sequences.
For radioactive cRNA probe synthesis, DNA was extracted and
then linearized. The reaction mix for both sense and anti-sense
RNA probes contained the following: 4 ml of35S-UTP (10 mCi/ml;
Amersham Biosciences, Piscataway, NJ), 3 ml35S-CTP (10 mCi/
ml; Amersham Biosciences), 2.0 ml 56transcription buffer, 1.0 ml
(n=4)were killed viarapid
Slides were removed from
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0.1 M DTT, 1.0 ml each of 10 mM ATP and GTP, 2.0 ml
linearized plasmid DNA, 0.5 ml RNase inhibitor, and 1.5 ml T3
RNA polymerase, in a total reaction volume of 25 ml. The mixture
was incubated at 37uC for 2 hours. After this period, 1 ml of
RNase-free DNase was added to the mixture and allowed to
incubate for an additional 15 min at room temperature. Each
probe was then purified using column-based chromatography
(BioRad Micro Bio-Spin Chromatography column, BioRad,
Hercules, CA), and its radioactivity was quantified using a liquid
scintillation analyzer. Following its preparation, each probe was
diluted in hybridization buffer (50% formamide, 20% filtered
water, 15% 206 SSC, 2% 506 Denhardt’s solution, 2% tRNA,
10% 0.5 M sodium phosphate buffer, 10% dextran sulfate) and
applied to dehydrated slides. A cover slip with 50–70 ml of
hybridization buffer, 1–26106DPM of radioactive probe, and
DTT at a final concentration of 10 mM, was placed on each slide.
Hybridization trays were prepared by lining the bottom of each
tray with filter paper, which was saturated with 50% formamide
buffer, and the slides were placed within. All trays were sealed and
placed at 55uC overnight. Approximately 18 hours later, cover
slips were removed and the slides were washed three times in 26
SSC for 5 min each. Next, slides were incubated in RNase A
(200 mg/ml in 10 mM Tris-HCl, pH 8.0/0.5 M NaCl) at 37uC
for 1 hour, then washed in a series of salt washes with increasing
stringency: 26SSC, 16SSC and 0.56SSC at room temperature
for 5 min each, followed by a one-hour incubation in 0.16SSC at
65–70uC. Finally, slides were dipped in distilled water and
dehydrated through graded ethanol solutions: 30 seconds each
in 50%, 75%, 90%, 95% 62, and 100% 62.
To determine the distribution of radioactive cRNA in situ, slides
were apposed to radiosensitive film (Kodak Biomax; Eastman
Kodak, Rochester, NY). Slides and the film were sealed within the
cassette and stored in complete darkness. Following a 5–19-day
exposure (exposure time depended on abundance of each mRNA
species), films were developed using a Kodak X-OMAT 2000A
processor (Eastman Kodak).
Image processing.
ISH autoradiograms were digitized using
a flatbed scanner (Microtek ScanMaker 1000XL, Microtek,
Carson, CA) at 1600 dpi. Digital images were then inverted and
each ISH signal was assigned a color as follows: TH – cyan, TPH2
– blue, preproNPQ – green, CRF – red, and ChAT – white. To
determine regional co-localization of preproNPQ with the other
mRNAs, images were then overlaid and aligned in Adobe
Photoshop CS2 (Adobe Systems, San Jose, CA). Regional co-
localization of signals was determined by mixing of the assigned
colors. Illustrations were prepared in Photoshop and Adobe
Illustrator CS2 (Adobe Systems). The signal was sharpened and
brightness and contrast were adjusted for presentation purposes.
Northern Blotting Using Human RNA Blot Probed with
Human NPQ cDNA
In order to determine if the preproNPQ transcript could be
detected in various human tissues, we used Ambion’s First Choice
Human Blot (a nylon membrane bound with 3 mg RNA from
various human tissues, Ambion Inc, TX). The blot was
prehybridized and probed with human NPQ cDNA prepared
using the above preproNPQ human primers and the human DNA
clone in pOTB7 vector from ATCC (Cat # 6710068, Manassas,
VA). This clone contained the putative sequence for human
preproNPQ, and the primers were used to isolate a 370 bp
preproNPQ sequence that was used as the cDNA probe for
hybridization to the RNA. Random-prime labeling of approxi-
mately 20–30 ng DNA was performed using
Klenow DNA polymerase, and after purifying the labeled probe
on a G-50 column, the labeled DNA probe was hybridized to the
nylon membrane overnight at 42uC. The membrane was washed
and exposed to film.
32P-dCTP and
Acknowledgments
We would like to thank Dr. Jackie Vazquez deRose for help with the
figures.
Author Contributions
Conceived and designed the experiments: KS NTZ IAK CRN SJW LT.
Performed the experiments: KS NTZ IAK SB CRN LT. Analyzed the
data: KS NTZ IAK LT. Contributed reagents/materials/analysis tools:
XX SJW LT. Wrote the paper: KS NTZ IAK LT.
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