The Secreted Protein Discovery Initiative (SPDI),
a Large-Scale Effort to Identify Novel Human
Secreted and Transmembrane Proteins:
A Bioinformatics Assessment
Hilary F. Clark,1Austin L. Gurney, Evangeline Abaya, Kevin Baker, Daryl Baldwin,
Jennifer Brush, Jian Chen, Bernard Chow, Clarissa Chui, Craig Crowley,
Bridget Currell, Bethanne Deuel, Patrick Dowd, Dan Eaton, Jessica Foster,
Christopher Grimaldi, Qimin Gu, Philip E. Hass, Sherry Heldens, Arthur Huang,
Hok Seon Kim, Laura Klimowski, Yisheng Jin, Stephanie Johnson, James Lee,
Lhney Lewis, Dongzhou Liao, Melanie Mark, Edward Robbie, Celina Sanchez,
Jill Schoenfeld, Somasekar Seshagiri, Laura Simmons, Jennifer Singh, Victoria Smith,
Jeremy Stinson, Alicia Vagts, Richard Vandlen, Colin Watanabe, David Wieand,
Kathryn Woods, Ming-Hong Xie, Daniel Yansura, Sothy Yi, Guoying Yu, Jean Yuan,
Min Zhang, Zemin Zhang, Audrey Goddard, William I. Wood, and Paul Godowski
Departments of Bioinformatics, Molecular Biology and Protein Chemistry, Genentech, Inc.,
South San Francisco, California 94080, USA
A large-scale effort, termed the Secreted Protein Discovery Initiative (SPDI), was undertaken to identify novel
secreted and transmembrane proteins. In the first of several approaches, a biological signal sequence trap in yeast
cells was utilized to identify cDNA clones encoding putative secreted proteins. A second strategy utilized various
algorithms that recognize features such as the hydrophobic properties of signal sequences to identify putative
proteins encoded by expressed sequence tags (ESTs) from human cDNA libraries. A third approach surveyed ESTs
for protein sequence similarity to a set of known receptors and their ligands with the BLAST algorithm. Finally, both
signal-sequence prediction algorithms and BLAST were used to identify single exons of potential genes from within
human genomic sequence. The isolation of full-length cDNA clones for each of these candidate genes resulted in the
identification of >1000 novel proteins. A total of 256 of these cDNAs are still novel, including variants and novel
genes, per the most recent GenBank release version. The success of this large-scale effort was assessed by a
bioinformatics analysis of the proteins through predictions of protein domains, subcellular localizations, and possible
functional roles. The SPDI collection should facilitate efforts to better understand intercellular communication, may
lead to new understandings of human diseases, and provides potential opportunities for the development of
[Supplemental material is available online at www.genome.org and at http://share.gene.com. The cDNA clone
sequences from this study have been submitted to GenBank under accession nos. AY358081–AY359127. The following
individuals kindly provided reagents, samples, or unpublished information as indicated in the paper: T. Wu.]
Discovery of novel human proteins provides new opportunities
for development of drug therapies for treatment of the wide
range of diseases for which there is still no cure. In other cases,
these proteins play an integral role in a disease state or the bio-
logical pathway leading to disease, and their identification and
characterization may lead to an understanding of disease para-
digms. Secreted and transmembrane proteins, in particular, have
properties that lend themselves to be utilized as therapeutic
agents or targets. They are accessible to various drug delivery
mechanisms, because they are presented on the cell surface or
within the extracellular space. A purified secreted protein or a
receptor extracellular domain can be utilized directly as a thera-
peutic (e.g., growth hormone), or may be targeted by specific
antibodies or small molecules. Important therapeutics have been
created that target proteins present on the cell surface in a spe-
cific cell type or disease state. Rituxan is an antibody therapeutic
targeting the B lymphocyte-specific CD20 protein and is an ef-
fective therapeutic in the treatment of non-Hodgkin’s lym-
phoma. Herceptin is an antibody therapeutic targeting the breast
carcinoma-specific HER2 protein and is an effective therapeutic
in the treatment of breast cancer.
A number of gene families of secreted and transmembrane
E-MAIL email@example.com; FAX (650) 225-5389.
Article and publication are at http://www.genome.org/cgi/doi/10.1101/
gr.1293003. Article published online before print in September 2003.
13:2265–2270 ©2003 by Cold Spring Harbor Laboratory Press ISSN 1088-9051/03 $5.00; www.genome.org
proteins related by homology have emerged that include mem-
bers known to have key roles in important biological processes
such as morphogenesis, cellular differentiation, angiogenesis, ap-
optosis, and modulation of the immune response, as well as dis-
ease processes such as cancer progression. These gene families
include tumor necrosis factors (Flavell 2002), growth factors
(Hackel et al. 1999; Cross and Claesson-Welsh 2001; Ornitz and
Itoh 2001; Danielsen and Maihle 2002), cytokines (Schooltink
and Rose-John 2002), chemokines (Onuffer and Horuk 2002),
interferons (Grandvaux et al. 2002), and angiopoietin-related
(Yancopoulos et al. 1998) protein families, as well as the protein
families of their receptors and other receptors such as the Toll-
like receptors (Armant and Fenton 2002), integrins (Dedhar
1999), and disintegrins (Yamamoto et al. 1999; Tang 2001).
In some cases, a protein may have therapeutic potential if it
is present in a disease state, even if it does not play a role in the
progression or maintenance of the disease. However, there are
many factors that influence a protein’s potential as an effective
and safe therapeutic or therapeutic target; the presence and
abundance in normal and diseased tissues, the subcellular local-
ization, the activity, and the biological role of the protein are just
some of these factors. Therefore, it is imperative to screen a large
number of proteins for a wide variety of such characteristics in
order to identify the most promising potential candidates for
drug development. Computational methods can be useful in pre-
dicting the likelihood of some of a protein’s characteristics in
order to focus further laboratory investigation on the proteins
with the most potential for playing a role in a disease state and
leading to a therapeutic. To facilitate the discovery of new thera-
peutic opportunities, we undertook a large-scale program of bio-
logical and computational strategies to identify and classify new
secreted and transmembrane proteins.
This effort to identify novel secreted and transmembrane human
proteins resulted in 1047 transcripts successfully cloned, repre-
senting 1021 genes (Table 1). A complete list of the GenBank
accession numbers of the cDNA clone sequences with details of
the analysis summarized in this publication is available as a
Supplementary Table. The success rate of the SPDI project can be
measured by the proportion of these genes that appear to encode
secreted or transmembrane proteins, which is 86% (879 genes). A
total of 13% (136 genes) appear to encode cytoplasmic and
nuclear proteins, and the subcellular localization could not be
predicted for 1% (6 genes). Because our identification of these
transcripts as representing novel genes, 77% (791) of them have
been submitted to GenBank from sources other than this SPDI
effort (Table 2). However, 25% of these cDNAs are still unique
transcripts. This includes 20% (209) that are variants of genes
currently represented in GenBank and 5% (47) that may repre-
sent completely novel genes.
A number of SPDI transcripts still represent novel genes at
the time of the submission of this work. Evidence of these being
bonafide genes includes ESTs, homology with known protein
domains, and orthology with a mouse gene. Such evidence is
present for most of the novel SPDI transcripts (Table 3). Tran-
scripts with none of this evidence may also represent bonafide
genes, but those with small predicted proteins may be more likely
to be partial transcripts or other artifacts. Nonetheless, almost all
of the SPDI transcripts that initially lacked supporting evidence
have been confirmed by cDNAs identified by others over the
The first approach to the identification of novel secreted
proteins was to exploit biological screens for the ability of cDNA
library-encoded fusion proteins to direct the secretion of a re-
porter protein. Yeast cells provide an easily manipulated system
for such screens for secreted proteins (Klein et al. 1996; Baker and
7 Transmembrane domain receptors
AMP binding proteins
C1q domain receptors
CUB/Sushi domain receptors
Cytokines and chemokines
Epidermal growth factor receptors
Epidermal growth factors
Fibroblast growth factors
Leucine-rich repeat receptors
Low-density lipoprotein receptors
Proteases and protease inhibitors
Scavenger receptor domain proteins
Semaphorins and plexin repeat receptors
Thrombospondins and ADAMs
Transforming growth factors
Trypsins and trypsin inhibitors
Tumor necrosis factor receptors
Tumor necrosis factors
Tyrosine kinase receptors
WNT and WNT induced signaling proteins
Other immunoglobulin superfamily
Other secreted proteins
Other transmembrane proteins
Total number of secreted and transmembrane proteins
Mitochondrial carrier proteins
Other cytoplasmic and nuclear
Total number of cytoplasmic and nuclear proteins
Total number of proteins with unpredicted localization6
Total number of SPDI genes1021
Gene families and categories as determined by computational assess-
ment of protein domains and sub-cellular localization, as well as
Clark et al.
Gurney 2000). Large libraries of cDNA fragments inserted before
the reporter gene can be screened, and positive yeast colonies
secreting the fusion reporter protein can then be identified. PCR
amplification of the cDNA insert from the yeast colony allows
sequence identification of cDNA clones encoding functional se-
One difficulty inherent in biological screens for secreted
proteins is that they encounter diminishing yields as the more
abundant novel proteins are discovered and the remaining novel
proteins become more rare. Computational methods, by com-
parison, are in principle, well suited to the identification of rare
genes, provided there is sequence information to analyze. The
overall SPDI strategy was to use both biological and computa-
tional methods to identify novel secreted and transmembrane
proteins from multiple sources of DNA sequence (Fig. 1). The
availability of very large collections of ESTs has greatly facilitated
the use of such computational strategies. Two algorithms that
detect the properties of signal peptides were developed and uti-
lized, Signal Sensor (C. Watanabe, unpubl.) and Sighmm (Zhang
and Wood 2003); both measure the hydropathy of the amino
terminus of DNA translations that may encode proteins. Both are
effective at identifying signal peptides with robust sensitivities
Some proteins known to be secreted or membrane bound
cannot currently be identified as such computationally and/or
do not possess a signal peptide. Additionally, limitations to the
EST collections result in some genes not being represented with
EST coverage containing amino-terminal sequence information.
However, these proteins may have amino acid sequence homol-
ogy to known secreted and transmembrane proteins. This ho-
mology may suggest a similar role and subcellular localization.
Thus, homology-based screening strategies can be a powerful
tool to identify putative secreted and transmembrane proteins.
We utilized a collection of known ligands and receptors of inter-
est as a homology-based method of identifying new members of
these protein families. The protein families used in this search
represent key players in cell–cell signaling, such as growth fac-
tors, cytokines, chemokines, and their receptors.
The recent availability of large-scale genomic sequence has
provided new opportunities to identify rare genes not abun-
dantly present within cDNA libraries and EST collections. The
presence of introns in genomic sequence requires that a gene-
prediction algorithm such as Genscan be used for gene identifi-
cation. We have utilized both signal-sequence detection strate-
gies and homology-based approaches to mine both predicted
genes and genomic sequence directly for the identification of
The SPDI effort utilized multiple gene-identification strate-
gies that were used at different times during the course of the
project, and genes already identified by one strategy were by-
passed with later strategies. For this reason, it is not possible to
evaluate which strategy was most effective at identifying secreted
and transmembrane proteins. However, the largest number of
genes were identified in this effort by computational signal se-
quence or homology detection from ESTs (Table 4). The smallest
number of genes were detected only from genomic sequence. EST
evidence was not sufficient for identification of these genes be-
cause of their rarity of expression, as EST coverage did not in-
clude a signal sequence, or because they are not highly homolo-
gous to the known ligands and receptors used to identify family
members. For some genes, multiple methods were required in an
iterative strategy in order to attain a full-length cDNA clone.
Often, this occurred for particularly long transcripts when a 5?-
truncated transcript was identified by EST mining, and then ge-
nomic sequence mining revealed the first exon of the gene. The
SPDI effort exemplifies the value of utilizing various complemen-
tary approaches of gene identification.
Many of the genes identified belong to gene families related
by homology, which are known to include important regulators
of key physiological processes. These include secreted proteins
such as cytokines, chemokines, and growth factors and their re-
ceptors. Other genes, such as those that apparently encode cyto-
plasmic or nuclear proteins, were also identified. In some cases,
this was due to the presence of domains such as protease do-
mains that can occur in proteins localized to either intracellular
or extracellular spaces. The families of proteins that were found
to have the greatest number of new members through this effort
were the immunoglobulin (Ig) domain and leucine-rich repeat
proteins. Combined, these two structural domains were present
in >10% of the proteins identified. Another 10% of the proteins
are clearly related to known classes of enzymes. A number of
these proteins appear to be localized within subcompartments of
secretory pathways and may have roles in regulating protein
post-translational modification (e.g., glycoslation). Perhaps sur-
prisingly, new members were identified for most of the major
known families of secreted proteins. In some cases, such as in the
interferon family, new members were identified despite the con-
siderable previous efforts to identify members of the family.
The success of this effort was due to the combined use of multiple
strategies for the identification of genes that encode secreted and
transmembrane molecules. Each strategy has different strengths
and limitations. The strategies were directed at both the source of
gene evidence, such as ESTs, and both predicted gene and exon
homology from genomic sequence, and at the method of detect-
ing putative proteins with the properties of secreted and trans-
membrane proteins including biological screens for secretion, al-
gorithms for detecting signal sequences, and homology searches
based on a collection of known secreted and transmembrane
proteins of interest.
The various methods described have differed in their success
at identifying particular types of genes. For instance, novel se-
creted genes without a recognizable relationship to other known
genes can perhaps only be identified with the biological or com-
putational signal-sequence detection methods. Conversely,
many secreted and transmembrane proteins of known gene fami-
lies do not have a detectable signal sequence (e.g., basic FGF), but
could be recognized by homology. The success rate of these
methods was also influenced by the timing of their introduction.
For example, the yeast signal trap screening was gradually dis-
continued as EST collections became larger and proved to be a
more efficient means of gene identification. Similarly, genomic
sequence mining was introduced only after EST mining had been
Novelty Assessment of Transcripts Identified
cDNAs Comparison to other GenBank cDNAs
791 Identity with GenBank cDNA currently in GenBank from
source other than this SPDI effort
Variant of gene with cDNAs in GenBank
No other GenBank cDNAs for this gene
Total number of cDNAs included in the SPDI collection
Determined just prior to this publication by BLAST algorithm against
GenBank using the ORF sequence (all sequences were absent from
GenBank when identified).
The Secreted Protein Discovery Initiative (SPDI)
The novelty of these proteins was a key factor in the criteria
for cloning them. Candidates that had identity to cDNA clone
sequences in GenBank were not pursued. Therefore, the genes
identified do not represent a complete collection of secreted and
transmembrane proteins. Large-scale efforts by others have also
identified comprehensive collections of cDNA clones for human
(Strausberg et al. 1999) and mouse (Kawai et al. 2001) genes.
Many proteins in the SPDI collection have already been
shown to have functions in important biological processes
through investigations with the cDNA clones identified here. Of
particular interest have been newly identified growth factors, cy-
tokines, tumor necrosis factors, and Toll family receptors. Angio-
genic mitogens stimulate growth of vascular endothelial cells,
which is critical to the development of vascular supply. EG-VEGF
induces proliferation, migration, and fenestration
in capillary endothelial cells derived from endo-
crine glands (LeCouter et al. 2001).
Cytokines and their receptors transmit sig-
nals that modulate the immune response. The IL-
17B and IL-17C cytokines induce the release of the
TNF-? and IL-1? cytokines from monocytic im-
mune cells (Li et al. 2000). The IL17E cytokine
binds the cytokine receptor IL17Rh1 and induces
activation of the NF-kB-signaling pathway and re-
lease of the proinflammatory cytokine IL-8 (Lee et
al. 2001). The IL-22 cytokine mediates the JAK-
STAT signaling pathway on binding its receptors
CRF2-4 and IL-22R (Xie et al. 2000). GLM-R is a
type-1 cytokine receptor that signals cellular pro-
liferation in the immune system (Ghilardi et al.
2002). The cytokine receptor TCCR is critical for
the generation of the adaptive immune response
mediated by T-helper cells of the Th1 subset (Chen
et al. 2000).
Tumor necrosis factors and their receptors are
involved in a number of physiological and patho-
logical responses. DR5 induces apoptosis in tumor
cells after binding Apo2L/TRAIL, and DcR1 and
DcR2 act as decoy receptors that inhibit this sig-
naling (Marsters et al. 1997; Sheridan et al. 1997).
Apo3L induces apoptosis and activatation of the NF-
kB signaling pathway after binding Apo3/DR3
(Marsters et al. 1998). DcR3 is a soluble decoy recep-
tor that inhibits apoptosis on binding Fas ligand
(Pitti et al. 1998). GITRL activates the NF-kB signal-
ing pathway on binding to its receptor GITR and
protects T-lymphocytes against apoptosis-induced
cell death (Gurney et al. 1999). XEDAR induces the
NF-kB signaling pathway on binding its ligand, an
isoform of ectodysplasin (Yan et al. 2000).
The innate immune system uses Toll family
receptors to signal for the presence of microbes
and initiate host defense. Bacterial lipoproteins
are potent activators of Toll-like receptor-2, medi-
ating both apoptosis and NF-kB signaling through
myeloid differentation factor 88 (Yang et al. 1998;
Aliprantis et al. 2000). Flagellin, the structural
component of bacterial flagella, is detected by
Toll-like receptor-5, which mediates inflammatory
responses to Salmonella (Gewirtz et al. 2001).
A number of the SPDI proteins have been im-
plicated in a wide range of other biological pro-
cesses, and further investigation of others is un-
derway (Pennica et al. 1998; Xie et al. 1999; Hol-
comb et al. 2000). Currently, microarray
expression data from a wide array of normal and
diseased cells and tissues is providing valuable information about
possible utility for many of these genes in the treatment of cancer
and immune diseases. The ongoing challenge for researchers is to
continue to develop and explore new genomic scale approaches
to best utilize the rich trove of sequence information that has
been made possible by this and other efforts to discover and
define the genes encoded within the human genome.
Biological Screens in Yeast Cells for Detection of
Recombinant gene libraries were constructed by replacing the
signal peptide encoded by the reporter gene with a library of
UPAR_LY6 68311 Yes
Characteristics of genes represented in GenBank solely by cDNAs from this SPDI pro-
gram as of July 16, 2003. EST evidence and domain prediction are described in Materials
and Methods, but only PFAM domains are noted here. Mouse orthologs have been
identified for some of these genes, but that assessment is not comprehensive.
Clark et al.
cDNA fragments. If a given cDNA fragment encodes a signal pep-
tide, the fusion protein may be secreted by a clonal colony of
yeast cells, enabling identification of functional signal se-
quences. Several reporter genes were utilized in these studies,
including invertase, amylase (Klein et al. 1996; Baker and Gurney
2000), and the yeast enzyme BAR1 (V. Smith, unpubl.). In brief,
yeast colonies were identified that were positive for secretion of
the reporter protein. The cDNA fragment contained within the
fusion gene was then isolated by PCR and sequenced. Those
cDNA fragments that appeared to encode ORFs containing signal
sequence motifs were then further characterized by isolation of
corresponding full-length cDNA clones.
Sequence Data Sources for Computational Screens
ESTs from both public (Lennon et al. 1996) and private (Incyte
Pharmaceuticals) collections were utilized. ESTs were mined from
consensus sequences of EST clusters, as well as individually. Ge-
nomic sequence was also mined, both directly from the human
high-throughput sequence (HTG) available in the GenBank da-
tabase, and through an internally compiled database of proteins
predicted by the Genscan algorithm (Burge and Karlin 1997)
from the HTG sequence. cDNA libraries were screened to isolate
corresponding full-length cDNA clones. In some cases, the cDNA
clones represented by an EST identified in these screens were
purchased from private (Incyte Pharmaceutical) and public
(Williamson 1999) sources.
Computational Screen for Signal Peptides
The Signal Sensor (C. Watanabe, unpubl.) and Sighmm (Zhang
and Wood 2003) algorithms were used to detect signal peptides
at the amino terminus of a putative protein sequence in an EST
or Genscan-predicted protein.
Computational Screen for Homology to Proteins
A collection of known secreted and transmembrane proteins
from gene families that include members with demonstrated bio-
logical function of particular interest (see Introduction) was used
as query sequences with the BLAST algorithm against EST, ge-
nomic, and predicted protein databases. The Pfam database was
queried using hmmpfam (Eddy 1998; Sonnhammer et al. 1998;
Bateman et al. 2002) with each of these known proteins to de-
termine protein domains that are represented in these protein
Novelty Assessment of Identified Transcripts
An automated computer algorithm was written to assess the nov-
elty of each sequence, using only the ORF queried by the BLAST
algorithm against GenBank. Identity was defined as at least 96%
identity over the length of the ORF minus no greater than 36 bp
(6 amino acids). A variant is defined as the top BLAST hit to a
GenBank entry without identity, but a match of at least 100 bp
with at least 96% identity. This identifies both splice variants and
truncated versions of the same protein. Sequences that had
no identity or variant in GenBank were further queried against
GenBank ESTs by BLAST. EST evidence was determined by a
match of at least 80 bp with at least 96% identity.
Prediction of Protein Domains and
An automated computational strategy was utilized to query each
protein translation with the Signal Sensor, Sighmm, Tmdetect (T.
Wu, unpubl.), hmmpfam (Eddy 1998), and Protcomp (Softberry,
Inc.) algorithms. As described earlier, the Signal Sensor and
Sighmm algorithms predict a secretion signal sequence. The Tm-
detect algorithm predicts a transmembrane domain. The hmmp-
fam algorithm queries the Pfam database of protein domains to
predict function domains of proteins that are related by sequence
homology. The Protcomp algorithm predicts the subcellular lo-
calization of a protein, on the basis of homology to well-
annotated proteins, a neural net, and various protein motifs.
Assignment of Transcipts to Gene Categories
The Gene Categories in Table 1 were determined by an iterative
analysis of protein domain and protein localization predictions
from various algorithms to assess the likely subcellular localiza-
tion and putative functional role of each protein. In general, the
genes have been divided into “Secreted and Transmembrane Pro-
teins” and “Cytoplasmic and Nuclear Proteins”, for the purpose
of evaluating the success of this effort to identify secreted and
transmembrane proteins. Further subcategories are delineated, in
which categories of particular interest or preponderance became
apparent. The categorization of proteins was determined first by
domains. Some curation was done to evaluate the hmmpfam
scores and determine whether the Pfam domain seemed valid, as
well as consideration of the signal sequence and transmembrane
domain predictions in that context. A number of genes had no
signal sequence, transmembrane, or Pfam domains predicted. In
this case, the Protcomp subcellular localization prediction was
used to categorize these genes as “Other Secreted”, “Other Trans-
membrane”, or “Other Cytoplasmic or Nuclear”.
We thank Thomas Wu for the Tmdetect algorithm and David
Carpenter for fruitful analysis and discussions.
The publication costs of this article were defrayed in part by
payment of page charges. This article must therefore be hereby
Assessment of the Contribution by Multiple
Primary identification method
Yeast signal sequence trap
Computational signal sequence detection from ESTs
Computational signal sequence detection from
Computational homology detection from ESTs
Computational homology detection from genomic
Determined by primary identification method.
SPDI flow diagram.
The Secreted Protein Discovery Initiative (SPDI)
marked “advertisement” in accordance with 18 USC section 1734 Download full-text
solely to indicate this fact.
Aliprantis, A.O., Yang, R.B., Weiss, D.S., Godowski, P., and Zychlinsky,
A. 2000. The apoptotic signaling pathway activated by Toll-like
receptor-2. EMBO J. 19: 3325–3336.
Armant, M.A. and Fenton, M.J. 2002. Toll-like receptors: A family of
pattern-recognition receptors in mammals. Genome Biol.
Baker, K. and Gurney, A.L. 2000. Method of selection for genes
encoding secreted and transmembrane proteins. In US Patent
Bateman, A., Birney, E., Cerruti, L., Durbin, R., Etwiller, L., Eddy, S.R.,
Griffiths-Jones, S., Howe, K.L., Marshall, M., and Sonnhammer, E.L.
2002. The Pfam protein families database. Nucleic Acids Res.
Burge, C. and Karlin, S. 1997. Prediction of complete gene structures in
human genomic DNA. J. Mol. Biol. 268: 78–94.
Chen, Q., Ghilardi, N., Wang, H., Baker, T., Xie, M.H., Gurney, A.,
Grewal, I.S., and de Sauvage, F.J. 2000. Development of Th1-type
immune responses requires the type I cytokine receptor TCCR.
Nature 407: 916–920.
Cross, M.J. and Claesson-Welsh, L. 2001. FGF and VEGF function in
angiogenesis: Signalling pathways, biological responses and
therapeutic inhibition. Trends Pharmacol. Sci. 22: 201–207.
Danielsen, A.J. and Maihle, N.J. 2002. The EGF/ErbB receptor family and
apoptosis. Growth Fact. 20: 1–15.
Dedhar, S. 1999. Integrins and signal transduction. Curr. Opin. Hematol.
Eddy, S.R. 1998. Profile hidden Markov models. Bioinformatics
Flavell, R.A. 2002. The relationship of inflammation and initiation of
autoimmune disease: Role of TNF super family members. Curr. Top.
Microbiol. Immunol. 266: 1–10.
Gewirtz, A.T., Navas, T.A., Lyons, S., Godowski, P.J., and Madara, J.L.
2001. Cutting edge: Bacterial flagellin activates basolaterally
expressed TLR5 to induce epithelial proinflammatory gene
expression. J. Immunol. 167: 1882–1885.
Ghilardi, N., Li, J., Hongo, J.A., Yi, S., Gurney, A., and de Sauvage, F.J.
2002. A novel type I cytokine receptor is expressed on monocytes,
signals proliferation, and activates STAT-3 and STAT-5. J. Biol. Chem.
Grandvaux, N., tenOever, B.R., Servant, M.J., and Hiscott, J. 2002. The
interferon antiviral response: From viral invasion to evasion. Curr.
Opin. Infect. Diseases 15: 259–267.
Gurney, A.L., Marsters, S.A., Huang, A., Pitti, R.M., Mark, M., Baldwin,
D.T., Gray, A.M., Dowd, P., Brush, J., Heldens, S., et al. 1999.
Identification of a new member of the tumor necrosis factor family
and its receptor, a human ortholog of mouse GITR. Curr. Biol.
Hackel, P.O., Zwick, E., Prenzel, N., and Ullrich, A. 1999. Epidermal
growth factor receptors: Critical mediators of multiple receptor
pathways. Curr. Opin. Cell Biol. 11: 184–189.
Holcomb, I.N., Kabakoff, R.C., Chan, B., Baker, T.W., Gurney, A.,
Henzel, W., Nelson, C., Lowman, H.B., Wright, B.D., Skelton, N.J., et
al. 2000. FIZZ1, a novel cysteine-rich secreted protein associated
with pulmonary inflammation, defines a new gene family. EMBO J.
Kawai, J., Shinagawa, A., Shibata, K., Yoshino, M., Itoh, M., Ishii, Y.,
Arakawa, T., Hara, A., Fukunishi, Y., Konno, H., et al. 2001.
Functional annotation of a full-length mouse cDNA collection.
Nature 409: 685–690.
Klein, R.D., Gu, Q., Goddard, A., and Rosenthal, A. 1996. Selection for
genes encoding secreted proteins and receptors. Proc. Natl. Acad. Sci.
LeCouter, J., Kowalski, J., Foster, J., Hass, P., Zhang, Z., Dillard-Telm, L.,
Frantz, G., Rangell, L., DeGuzman, L., Keller, G.A., et al. 2001.
Identification of an angiogenic mitogen selective for endocrine
gland endothelium. Nature 412: 877–884.
Lee, J., Ho, W.-H., Maruoka, M., Corpuz, R.T., Baldwin, D.T., Foster, J.S.,
Goddard, A.D., Yansura, D.G., Vandlen, R.L., Wood, W.I., et al.
2001. IL-17E, a novel proinflammatory ligand for the IL-17 receptor
homolog IL-17Rh1. J. Biol. Chem. 276: 1660–1664.
Lennon, G., Auffray, C., Polymeropoulos, M., and Bento Soares, M.
1996. The I.M.A.G.E. consortium: An integrated molecular analysis
of genomes and their expression. Genomics 33: 151–152.
Li, H., Chen, J., Huang, A., Stinson, J., Heldens, S., Foster, J., Dowd, P.,
Gurney, A.L., and Wood,W.I. 2000. Cloning and characterization of
IL-17B and IL-17C, two new members of the IL-17 cytokine family.
Proc. Natl. Acad. Sci. 97: 773–778.
Marsters, S.A., Sheridan, J.P., Pitti, R.M., Huang, A., Skubatch, M.,
Baldwin, D., Yuan, J., Gurney, A., Goddard, A.D., Godowski, P., et al.
1997. A novel receptor for Apo2L/TRAIL contains a truncated death
domain. Curr. Biol. 7: 1003–1006.
Marsters, S.A., Sheridan, J.P., Pitti, R.M., Brush, J., Goddard, A., and
Ashkenazi, A. 1998. Identification of a ligand for the
death-domain-containing receptor apo3. Curr. Biol. 8: 525–528.
Onuffer, J. and Horuk, R. 2002. Chemokines, chemokine receptors and
small-molecule antagonists: Recent developments. Trends Pharmacol.
Sci. 23: 459–467.
Ornitz, D. and Itoh, N. 2001. Fibroblast growth factors. Genome Biol.
Pennica, D., Swanson, T.A., Welsh, J.W., Roy, M.A., Lawrence, D.A., Lee,
J., Brush, J., Taneyhill, L.A., Deuel, B., Lew, M., et al. 1998. WISP
genes are members of the connective tissue growth factor family
that are up-regulated in wnt-1-transformed cells and aberrantly
expressed in human colon tumors. Proc. Natl. Acad. Sci.
Pitti, R.M., Marsters, S.A., Lawrence, D.A., Roy, M., Kischkel, F.C., Dowd,
P., Huang, A., Donahue, C.J., Sherwood, S.W., Baldwin, D.T., et al.
1998. Genomic amplification of a decoy receptor for Fas ligand in
lung and colon cancer. Nature 396: 699–703.
Schooltink, H. and Rose-John, S. 2002. Cytokines as therapeutic drugs. J.
Interfer. Cyto. Res. 22: 505–516.
Sheridan, J.P., Marsters, S.A., Pitti, R.M., Gurney, A., Skubatch, M.,
Baldwin, D., Ramakrishnan, L., Gray, C.L., Baker, K., Wood, W.I., et
al. 1997. Control of TRAIL-induced apoptosis by a family of
signaling and decoy receptors. Science 277: 818–821.
Sonnhammer, E.L., Eddy, S.R., Birney, E., Bateman, A., and Durbin, R.
1998. Pfam: Multiple sequence alignments and HMM-profiles of
protein domains. Nucleic Acids Res. 26: 320–322.
Strausberg, R.L., Feingold, E.A., and Klausner, R.D. 1999. The
mammalian gene collection. Science 286: 455–457.
Tang, B.L. 2001. ADAMTS: A novel family of extracellular matrix
proteases. Intl. J. Biochem. Cell Biol. 33: 33–44.
Williamson, A.R. 1999. The Merck gene index project. Drug Discovery
Today 4: 115–122.
Xie, M.H., Holcomb, I., Deuel, B., Dowd, P., Huang, A., Vagts, A., Foster,
J., Liang, J., Brush, J., Gu, Q., et al. 1999. FGF-19, a novel fibroblast
growth factor with unique specificity for FGFR4. Cytokine
Xie, M.H., Aggarwal, S., Ho, W.H., Foster, J., Zhang, Z., Stinson, J.,
Wood, W.I., Goddard, A.D., and Gurney, A.L. 2000. Interleukin
(IL)-22, a novel human cytokine that signals through the interferon
receptor-related proteins CRF2-4 and IL-22R. J. Biol. Chem.
Yamamoto, S., Higuchi, Y., Yoshiyama, K., Shimizu, E., Kataoka, M.,
Hijiya, N., and Matsuura, K. 1999. ADAM family proteins in the
immune system. Immunol. Today 20: 278–284.
Yan, M., Wang, L.C., Hymowitz, S.G., Schilbach, S., Lee, J., Goddard, A.,
de Vos, A.M., Gao, W.Q., and Dixit, V.M. 2000. Two-amino acid
molecular switch in an epithelial morphogen that regulates binding
to two distinct receptors. Science 290: 523–527.
Yancopoulos, G.D., Klagsbrun, M., and Folkman, J. 1998.
Vasculogenesis, angiogenesis, and growth factors: Ephrins enter the
fray at the border. Cell 93: 661–664.
Yang, R.B., Mark, M.R., Gray, A., Huang, A., Xie, M.H., Zhang, M.,
Goddard, A., Wood, W.I., Gurney, A.L., and Godowski, P.J. 1998.
Toll-like receptor-2 mediates lipopolysaccharide-induced cellular
signalling. Nature 395: 284–288.
Zhang, Z. and Wood, W.I. 2003. A profile hidden Markov model for
signal peptides generated by HMMER. Bioinformatics 19: 307–308.
Received February 23, 2003; accepted in revised form July 28, 2003.
Clark et al.