Supplementary Information for Fahrer et al.
A genomic view of immunology.
Aude M. Fahrer, J. Fernando Bazan1, Peter Papathanasiou, Keats A. Nelms, Christopher
C. Goodnow.
Medical Genome Centre, John Curtin School of Medical Research, Australian National
University, Canberra, Australia, and 1Dept. of Molecular Biology, DNAX Research
Institute, CA, USA.
Supplementary information.
Protein homology searches:
Several excellent databases are freely available over the internet to search for protein
homologues. These include NCBI [http://www.ncbi.nlm.nih.gov/BLAST/], Interpro
[http://www.ebi.ac.uk/interpro/], SMART [http://smart.embl-heidelberg.de/], CLUSTR
[http://www.ebi.ac.uk/clustr/] and BIOSPACE [http://biospace.stanford.edu/].
Problems arise, however when trying to interrogate the human genome sequence. Most
of the contigs are heavily fragmented; broken into, for example, 15-20 pieces of 1kb,
which have been randomly ordered, and separated by runs of “N”s. This leads to
problems when trying to predict open reading frames and virtual transcribed sequences
(VTS). Most efforts at trying to order the genome, and in predicting VTS are being done
by private companies, and are not freely or even publicly available. An exception is the
University of California, Santa Cruz effort which is publicly available
[http://genome.ucsc.edu/] but can’t be directly queried by homology searches. For this
reason, the databases compiled by the Sanger Centre (discussed below), which
incorporate novel predicted peptides from the human genome sequence, as well as known
proteins turned out to be exceptionally useful.
Some examples of different types of homology searches, in increasing order of difficulty,
are shown. The first two are accessible to the averagely computer-literate immunologist.
The last requires much more specialised bioinformatics.
1. Looking for protein with previously identified motifs.
The TNFR family is an extremely important group of molecules which can control either
the proliferation or the apoptosis of lymphocytes. Despite having low overall homology
(20-25%), the TNFR family is defined by conserved cysteine residues in the extracellular
ligand binding domain 1. The family can be divided into two groups based on the
presence or absence of a death domain 2.
Typing “TNFR” into the Interpro search site [http://www.ebi.ac.uk/interpro/] allows you
to retrieve accession IPR001368 for the TNFR/NGFR family cysteine-rich region
domain. If this is used to search the Sanger centre protein tables
[http://www.sanger.ac.uk/Users/lmc/Ensembl4/collapsed.families/html/index.html], 31
human proteins containing this domain are found. These were compared against the
known TNFR family members enumerated in a recent review 2. Multiple nomenclatures
for each protein member were quickly unravelled using NCBI’s Online Mendelian
Inheritance in Man (OMIM) website [http://www.ncbi.nlm.nih.gov/].
It was found that 21 of the 22 known TNFR family members were represented, the
exception being TNFR superfamily member 18. Several members were represented two
or three times. Five proteins in the list did not immediately match known TNFR family
proteins. One of these was the very recently published TAJ 3. The other four were
potentially novel proteins with IGI_ accession numbers. By performing BLAST searches
[http://www.ncbi.nlm.nih.gov/blast/blast.cgi?Jform=1] on these four proteins, it was
found that two corresponded closely to known TNFR family members OPG and DR5.
IGI_M1_ctg13384_53 was similar (but not identical) to OX40, and IGI_M1_
ctg13980_78 was similar, but only over its N-terminal half, to CD30. Thus, over 95% of
the known TNFR family members were represented in these tables, and two potentially
new and uncharacterised members of the family were rapidly pinpointed.
b) If your protein family of interest is not represented by an Interpro domain:
The CD80/CD86 molecules (also known as B7.1/B7.2) are members of the
immunoglobulin superfamily and are expressed on antigen presenting cells. They share
26% identity and 46% homology at the amino acid level. They both interact with CD28
and CD152 (CTLA-4) molecules on the surface of T cells to transmit either costimulatory or inhibitory signals to the T cell. Recently two new homologs of CD80 and
CD86 have been identified 4: ICOSL which binds to the T cell receptor ICOS; and B7H1 which binds to the inhibitory receptor PD-1 5. Crosslinking of ICOS is also costimulatory for T cells, but leads to a different type of T cell response than co-stimulation
through CD28. Clearly then, the CD80/CD86 family of proteins is critical for
modulating T cell responses. Can we find other members of the family?
Since these proteins are not represented in Interpro, we looked for new members using
BLAST. Most useful in this case is the position specific iterated PSI-BLAST search 6.
This allows searching based on the similarity to several proteins at once, resulting in
much greater sensitivity in identifying proteins with weak, but potentially biologically
significant, similarities.
PSI-BLAST can be accessed through NCBI
[http://www.ncbi.nlm.nih.gov/blast/psiblast.cgi], and can be used to search the Genbank
databases. Unfortunately, novel peptides predicted from the human genome sequence are
not yet available for searching. With somewhat more effort, PSI-BLAST can also be
downloaded and used to search other databases. We chose this second option, and used
the International Protein Index (version 1) compiled by Ewan Birney at the Sanger Centre
[http://www.ensembl.org/IPI/]. We downloaded the expanded peptide database
[ftp://ftp.sanger.ac.uk/pub/birney/humanproteome].
By first doing a normal BLAST search against CD80, we found that the first 5 matches
(with E values) were: CD80 (e-161); CD86 (5e-12); 2 proteins corresponding to ICOSL
(both 2e-09) and B7-H1 (3e-08). A PSI-BLAST search based only on similarity to CD80
and CD86 identified both ICOSL sequences and B7-H1 again, but with higher
significance scores (4e-15 and 3e-12 respectively).
A PSI-BLAST search based on all 5 sequences (using e-7 as a significance cut-off)
identified 21 proteins.
-Nine of these represented members of the butyrophilin family. Butyrophilin is a cell
surface protein also found in breast milk and has previously been identified as having a
high similarity to the B7 family 7.
-Three of the proteins corresponded to signal regulatory protein (SIRP)--1 and one to
SIRP--1. Both of these are transmembrane proteins. SIRP--1 is an inhibitory
receptor, expressed by splenic macrophages, which binds to the CD47 self marker on
erythrocytes preventing their elimination 8. Less is known about SIRP--1, but it seems
to be involved in the activation of myeloid and dendritic cells.
-Four other proteins corresponding to previously cloned genes were identified: HHLA2,
a human endogenous retrovirus sequence encoding a potentially secreted protein
expressed in several tissues, including lymphocytes; MCAM, a melanoma adhesion
protein apparently involved in tumour progression; CXADR, a surface molecule of
unknown function; and VEJAM a vascular endothelial junction associated molecule,
potentially involved in lymphocyte homing.
In addition four novel proteins were found: IGI_M1_ctg1747_10 (2e-18),
IGI_M1_ctg16974_7 (6e-10), Q9UJP1 (2e-08) and IGI_M1_ctg12704_19 (9e-08).
Thus, based on some of the known proteins identified by the search, it is quite likely that
some of the less characterised proteins could be involved in the co-stimulation or
modulation of lymphocytes, macrophages, or other cell types.
If downloading PSI-BLAST and a database is impractical, an alternative is to take the
protein sequences of interest, and run them through the program BLOCKMAKER 9 to
align them [http://blocks.fhcrc.org/]. The alignment can then be run through the program
COBBLER 10 to obtain a consensus sequence motif. This motif can then be used to
BLAST against any database of choice. We found that this works well but, since the
BLAST search is based on a single sequence (albeit a consensus one), gives less
significant E values than the PSI-BLAST based method.
c) Finding novel molecules using structural queues:
Cytokines represent an important class of immune regulators with protein folds that are
particularly pliable to sequence divergence, a finding that emerges from the comparison
of well-conserved three-dimensional structures that feature the faintest of chain
similarities 11. In fact, striking family relationships have often emerged only after the
resolution of prototype cytokine folds; for example, cementing the distant similarity
between fibroblast growth factors (FGFs) and interleukin-1 (IL-1)-like molecules 12, or
suggesting a link between TNF proteins and an extended family of complement C1q-like
cytokines 13. These unexpected findings often broaden our view of the evolutionary
emergence and biological functions of cytokines. The challenge in this genomic age is to
detect novel molecules--otherwise buried in the dark recesses of sequence databases--that
may have weak or unapparent ties to existing cytokine groups; we can do this best with
computational techniques that are being used to sensitively annotate genome-derived
sequences, and fuse knowledge of the structural templates with sensitive sequence
searching and prediction routines 14.
The superfamily of haemopoietic cytokines is distinguished by a unique 4-helix bundle
fold that engages a special class of transmembrane receptors 11. While difficult to align
by sequence similarity, the helical scaffolds of these cytokines reveal faint, subfamilydistinctive motifs when superimposed--aside from the expected register of core
hydrophobic residues. Taking the best conserved 'D' helix (fourth and final in the bundle
sequence) alignment of a diverse series of IL-6-like cytokine structures (comprising IL-6,
GCSF, CNTF, OSM, LIF, alongside a carefully arrayed set of CT-1, IL-11 and IL-12
sequences), both weighted profiles, position-specific scoring matrices (PSSMs) and
hidden Markov models (HMMs) were constructed and used to iteratively search both
EST and genomic databases. This roughly 35 amino acid-long profile effectively
collected all extant IL-6-type sequences, as well as a set of novel, predicted ORFs, that
were then used to clone their complete gene sequences. Among these orphan cytokines is
a molecule that distantly resembles CNTF and is variously called Novel Neurotrophin-1
(NNT-1) or Cardiotrophin-like Cytokine (CLC)--not surprisingly, this molecule has
recently been shown to coopt the CNTF receptor complex to signal 15. Another outlier
sequence has a far resemblance to the p35 subunit of IL-12, and it has recently been
shown that it competes for binding to the p40 chain (creating a cytokine now labelled as
IL-23), binding then to a receptor complex that includes elements of the IL-12 signalling
machinery 16. In cases where no sequence similarity is detectable--but secondary
structure prediction indicates, for example, a compatible register of helices and loops--
fold recognition or threading techniques are capable of teasing out a reliable alignment
of a novel sequence with a helical cytokine template 17.
Other forms of homology searches.
As opposed to protein homology, functionally related genes may also be revealed by
homology in the DNA sequence of their promoters. Gene expression profiling on DNA
microarrays has revealed the co-regulation of functionally related genes. In principle this
should be reflected by presence of very similar combinations of transcription factor
binding sites in transcriptional control elements, such as the combined NFAT/NFkB
motif in several cytokines.
Correlating heritable traits with specific gene products.
In practice, the genome databases are not yet at the point of listing all genes contained
within a genomic interval between two markers, although it is likely that they will be
within 6-12 months. As an example, consider that an autoimmune susceptibility trait has
been mapped to chromosome 21 between markers D21S49 and D21S171. These flanking
markers can be used to query NCBI’s Entrez Map Viewer
[http://www.ncbi.nlm.nih.gov/cgi-bin/Entrez/hum_srch?chr=hum_chr.inf] which
integrates human sequence and map data from a variety of sources. The types of maps
include sequence, cytogenetic, genetic linkage, radiation hybrid, and YAC contig.
Searching for the marker “D21S49” on chromosome “21” retrieves data from the STS
sequence map only. Using the “Display settings” function on the STS map, the other
flanking marker “D21S171” may now be entered. With the “STS Map” maintained as the
“Master Map”, the relative positions of items on additional maps may be viewed by
selecting from the various maps available (of which the “Genes_Sequence” should
theoretically be the most accurate). At this stage it is also useful to increase the “Page
Size” so that all markers in the chosen region are displayed. The list should now contain
the genes that exist in the region between these markers. However, whilst D21S49 shows
up at the top end, D2S171 is not at the bottom. Upon going back and re-searching Map
Viewer for this marker it is found that is has neither been sequenced or positioned on any
map available. Whilst this result seems to fly in the face of chromosome 21 being
completely sequenced, it could simply be the case that the online version has not yet been
annotated with all genetic markers.
Two search options are now available. The first is to search the NCBI GenBank menu
[http://www.ncbi.nlm.nih.gov/] as the non-redudant sequence of chromosome 21 was
divided into 340-kb segments and registered in the GenBank databases under accession
numbers AP001656-AP001761. Searching for “D21S171” finds the “AP001754”entry in
which this marker resides (the left primer starts from basepair position 288406). An
alternate search may be performed using BLAST on the human genome
[http://www.ncbi.nlm.nih.gov/genome/seq/page.cgi?F=HsBlast.html&&ORG=Hs] using
the left primer for D21S171 (this information is available from the STS-Based Map of the
Human Genome search engine at [http://carbon.wi.mit.edu:8000/cgibin/contig/sts_info?database=release]; see also 18). This reveals one BLAST Hit aligned
across the entire sequence with the reference contig “NT_002835” or “Hs21_2979”, and
upon this contig resides the “AP001754” clone.
The “Display settings” function on the Map Viewer may now be searched again with the
flanking markers “D21S49” and “AP001754”. Maintaining the “STS Map” as the
“Master Map” and selecting the “GenBank” and “Genes_Sequence” maps as additionals,
brings up the region between these markers, albeit on the two different maps within
which these markers reside. In the interval between the two markers, 17 candidate genes
can be seen on the complete sequence map. One gene listed is a clear candidate, AIRE,
which has recently been shown to carry loss-of-function mutations in a rare Mendelian
syndrome, autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy 19, 20.
Gaps in annotation are nonetheless significant for chromosome 21, a relatively finished
chromosome, as six known genes in the literature 21 between D21S49 and D21S171
(including PFKL liver-type 1-phosphofructokinase gene, positioned next to AIRE on the
sequence) are not shown on Map Viewer. It is also likely that gene predictions are
currently incomplete and will improve with alignment of the human and mouse
sequences. By moving between different databases and using strategies outlined in a
recent review 22, one can piece together a fuller picture of what is placed where on the
sequence map.
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