BACKGROUND
leucine-rich repeats  are protein-ligand interaction motifs found in a large number of proteins of diverse structure, localization and function in bacteria, fungi, plants and animals  <cit> .
many of these have well-known functions in the innate immune system  <cit> .
many others, especially those with extracellular lrrs , are involved in various aspects of nervous system development  <cit> .
in both cases, the nature of the lrr motifs is important for generating a diversity of interactions, with exogenous factors in the immune system and with the huge number of different cell types in the developing nervous system.
the structure of lrr motifs and their arrangement in repetitive stretches of variable length generate a versatile and highly evolvable framework for the binding of diverse proteins and non-protein ligands.
seven classes of lrr have been defined  <cit> ; .
within animals, four separate types are recognised, three typically intracellular and one extracellular.
whether all these different classes are evolutionarily related by descent or represent convergent evolution is open to debate  <cit>  but they all share a characteristic structure.
each repeat is typically 19– <dig> amino acids long and has a well-conserved n-terminal stretch of 9– <dig> amino acids that is characterized by precisely-positioned hydrophobic residues  and that forms a β-strand and a c-terminal stretch of 10– <dig> amino acids that is more variable in length, sequence and structure.
the arrangement of multiple repeats in tandem generates a horseshoe-shaped solenoidal structure, with the β-strands stacking to form the concave surface and the variable stretches forming the convex surface  <cit> .
most lrr regions typically also have both n-terminal and c-terminal cap regions, which shield the hydrophobic core of the lrr structure.
in extracellular proteins these regions  are defined by precisely positioned cysteine residues  <cit> .
lrr proteins, both intracellular and extracellular, have well-characterized functions in the innate immune system that are similar from plants to mammals  <cit> .
the extracellular lrr  proteins in animals include the toll-like receptors , a family of transmembrane proteins characterized by an lrr region, a transmembrane  domain and a cytoplasmic toll/il- <dig> receptor  domain.
this family has expanded in vertebrates to allow detection of a diverse set of antigens  <cit> .
in flies, the tlr family has also expanded, where, in addition to roles in immunity for some of these proteins  <cit> , many are required for various aspects of embryonic and nervous system development  <cit> .
tol- <dig> in worms is also important in development, possibly contributing to a code of molecules defining neuronal connectivity  <cit> .
recent reports indicate that some mammalian tlr genes may also be expressed and function in neurons  <cit> .
a large number of other elrr proteins have been implicated in various aspects of neural development, genetically in flies  <cit>  and in mammals in assays of neurite outgrowth,  <cit> , fasciculation  <cit>  and/or synapse formation  <cit> .
some of these contain, in addition to the extracellular lrr domain, immunoglobulin  or fibronectin type- <dig>  domains .
in some cases, the functions of elrr proteins are mediated by homophilic interactions  <cit> .
in other cases they are mediated by the binding of other proteins in cis  <cit>  and in trans  <cit> .
several elrr proteins have been found to modulate the signaling of various growth factor pathways .
surprisingly, apart from the tlr genes  <cit>  and small secreted proteoglycans  <cit> , relatively few elrr genes have been studied genetically in mice.
among the ones that have, examples of phenotypic effects in the nervous system include increased plasticity, sprouting and nerve regeneration  <cit> , and defects in axon guidance and cell migration  <cit> , learning and memory  <cit> , myelination  <cit>  and neuronal survival  <cit> .
the importance of this class of proteins for nervous system development in humans is apparent from the large number of examples implicated in neurological or psychiatric disorders .
these include epilepsy  <cit> , tourette's syndrome  <cit> , night blindness  <cit> , congenital insensitivity to pain   <cit> , and possible links to alzheimer's disease  <cit> .
despite the growing number of elrr proteins implicated in nervous system development or disease this family of proteins has received far less attention as a class than other better characterized families like the immunoglobulin  <cit>  and cadherin  <cit>  superfamilies.
in particular, there have been no systematic surveys of the genomic complement of these proteins or investigation of their evolutionary relationships.
we therefore set out to catalogue the entire extracellular leucine-rich repeat proteome of four organisms: caenorhabditis elegans, drosophila melanogaster, mus musculus and homo sapiens.
we used a hierarchical clustering system to analyse within and between-species relationships, revealing independent diversification and expansion of subfamilies in each species and rapid sequence divergence.
these analyses highlight the large number of novel, uncharacterized elrr proteins in each of these genomes, including several novel subfamilies.
a number of these show highly restricted expression in the nervous system in mouse or fly.
RESULTS
bioinformatics strategy
we began by obtaining whole proteome sets of known and predicted proteins from each of the four species, as described in methods.
our initial approach was to filter the starting proteome datasets using transmembrane  and signal peptide prediction programs  to identify transmembrane or secreted proteins and then to filter that set using a motif recognition program  to identify the subset with motifs of interest.
however, this approach using serial filters missed a number of known axon guidance molecules because tmhmm or signalp misclassified them or pfam did not detect specific motifs.
we therefore included a number of other tm-prediction and motif-recognition programs, including a customised program to look for lrr domains  in the pipeline.
in addition, we first performed a clustering step using tribemcl  <cit>  on the entire proteome sets so as to identify related proteins even where these programs failed to detect specific motifs or architectures.
rather than using any of these programs or a combination of them as strict filters we generated a database containing all the results that could be browsed or searched using various criteria to extract particular gene families of interest .
clustering
tribemcl is a program designed to cluster proteins into related families based on simultaneous examination of all pairwise blast similarity scores  <cit> .
this program uses a markov cluster algorithm that is particularly well suited to cluster highly divergent proteins with repeated domains into separate subfamilies, a task for which multiple alignment programs are not appropriate.
the markov cluster algorithm is based on simulated 'flow' through a network or graph, where each node is a gene and each connection is weighted by the symmetric pairwise blast score.
a random walk of a certain length from node to node through this network, which begins within a small cluster of interconnected genes will have a higher probability of ending up within that cluster than crossing to a gene that is only related to one of them.
the results of many simulated random walks thus define the clusters.
each time this process is reiterated the links within the clusters that emerge are given a higher weighting and the links that were not used are downgraded.
multiple rounds of this process eventually lead to completely separate clusters.
there are two parameters that can be varied that affect the clustering; the first is the e-value cutoff of the blast scores that are used, which determines the initial set of connections considered.
the second is the inflation parameter; this determines how strongly the links are upgraded with each iteration.
a higher inflation parameter increases the 'granularity' of the output; i.e., it generates a larger number of smaller clusters.
we used a number of e-value cutoffs  and inflation parameters  and compared the output produced.
the output from tribemcl, for any given e-value cutoff and inflation parameter can be viewed as a list of genes organized into clusters with a number assigned to each cluster .
we reasoned that hierarchical sorting of genes across various inflation parameters should yield a tree-like structure, with larger clusters at low inflation parameters splitting into more discrete clusters at higher inflation parameters.
at each e-value cutoff we therefore sorted the list of genes first at inflation parameter  <dig> , then  <dig>  then  <dig>   <dig> and  <dig>  for the most part, increasing inflation parameter does lead to splitting of large clusters into smaller clusters and yields a tree-like arrangement of genes with relationships apparent across various levels .
identification of lrr motifs
to identify lrr proteins, the database was searched for all genes containing at least one lrr, lrr-nt or lrr-ct predicted by either smart or pfam.
the cutoff values used were based on analysis of the results for proteins with known architecture .
this analysis yielded a total of  <dig>  entries.
these include both genes with intracellular lrrs and those with extracellular lrrs.
it also contains isoforms for many genes.
to screen out false positives we used the following criteria: if only one lrr was predicted in a gene and only by one of the programs and it was not predicted in either the mouse or human orthologue , or in other members of a closely-related cluster then it was considered a false positive and discarded.
identification of extracellular lrr proteins
comparison of several tm-prediction programs suggests that tmhmm is the most reliable, although it is also the most selective  <cit> .
a quick survey of some known tm receptors revealed that tmhmm failed to identify tm domains in several of them, including robo <dig> in mammals and kekkon <dig> and  <dig> in flies, for example.
for that reason we also used two other programs, hmmtop  <cit>  and tmpred  <cit>  to search for tm domains.
at least one of these three programs successfully detected the tm domain in all the known tm receptors examined .
signalp  <cit>  was used to detect signal peptides.
this suffered from poor prediction of 5' exons for many mammalian genes, which was solved by manual curation .
the gpi-prediction program big-pi  <cit>  identified a small number of gpi-linked proteins, including all the known gpi-linked proteins such as connectin, ngrs and nyx .
a number of other genes were tentatively assigned to the gpi-class by manual inspection based on the presence of a characteristic short c-terminal hydrophobic stretch .
in addition to examining the convergent evidence from these various programs to identify elrr proteins we used three additional criteria.
the first is the type of lrr predicted: extracellular proteins typically contain lrr types designated lrr_ <dig>   <dig> or  <dig> by pfam or lrr_typical by smart, while the intracellular proteins have lrr_ri or lrr_sd <dig> .
second, the prediction of an lrr-nt and/or lrr-ct domain was taken as evidence for extracellular localization.
third, especially at low e-value stringencies , the majority of extracellular lrr proteins cluster together with tribemcl in one large group , distinct from the intracellular proteins.
using these criteria in addition to the data from the prediction programs described above we collected what we believe is a comprehensive set of extracellular lrr proteins across worm, fly, mouse and human.
we call these the elrr proteome.
manual curation of extracellular sequences
to reduce the complexity of the final data set a single protein isoform was chosen for each gene and all others were removed .
many peptides that we expected to be extracellular because of orthology, clustering or domain structure did not have a predicted signal peptide.
upon manual inspection of the sequences it was discovered that many gene predictions in ensembl, especially for mammalian genes, were missing the 5'-most exon encoding the signal peptide.
for many such genes we identified the 5' exon and the full coding sequence in a sequence from another database and/or by searching with an orthologous gene from mouse or human.
in other cases the 5' predicted sequence extended past the apparent true methionine start codon, which could be recognised by conservation and the presence of the signal sequence.
we identified two fly genes that have been incorrectly annotated in ensembl as two separate genes each.
cg <dig> and cg <dig> represent the 5' and 3' ends of a cdna encoded by ab <dig>  a new member of the lgr <dig> family.
similarly, cg <dig> and cg <dig> represent the 5' and 3' ends of the fish-lips  gene, encoded by aav <dig>  <cit>  which is related to tartan and capricious  <cit> .
we detected one similar mis-annotation in the worm database  and presume that this type of error may also have occurred for some mammalian sequences.
all the manually curated gene sequences are provided .
these curated sequences were fed back into the starting database and the blast and clustering analyses were re-performed to ensure that spurious results had not been generated by incorrect sequences.
defining consensus architectures
in order to derive a consensus architecture for each gene we compared the results of smart and pfam and the tm-prediction programs.
even at very low stringency some lrrs in proteins with known numbers of such repeats were missed by hmmpfam using the smart and pfam databases.
this includes a number of somewhat degenerate lrrs in lrrc <dig> proteins  <cit> , for example, as well as atypical lrr-ct domains in small proteoglycans and g-protein-coupled receptors  <cit> .
for this reason, using a similar strategy to smits and colleagues  <cit> , we wrote a customised program, lrrscan, to search for a more inclusive minimal consensus that defines lrrs as well as searching for consensus sequences derived from non-canonical lrr-ct domains .
lrrscan was successful in identifying all the predicted lrrs in lrrc <dig> proteins, including atypical or degenerate ones  <cit> , and additional lrrs in many other proteins that were not detected by hmmpfam with smart or pfam.
the output from lrrscan and hmmpfam was compared for all proteins  and a consensus architecture including number of lrrs and presence of lrr-nt and lrr-ct domains was derived by manual curation.
the consensus matches the architecture of a number of elrr proteins with published structures  <cit> , allowing for semantic differences in how the lrrs are counted.
the final lrr before the lrr-ct domain  often contains only the first subdomain of nine residues; following the convention of matsushima and colleagues  <cit>  we count this as one repeat rather than part of the lrr-ct domain, which in some cases may cause an apparent discrepancy with published reports.
we also do not count in the total number of repeats putative lrrs which overlap with lrr-nt or lrr-ct domains, as has been done in some published cases  <cit> .
a consensus topology for each protein was also derived by comparison of the signal peptide, gpi anchor and tm-prediction programs.
a full list of all the elrr proteins is provided  and a sample is shown in figure  <dig>  these are sorted hierarchically across inflation parameters at an e-value cutoff of - <dig>  clustering results at e- <dig> and e- <dig> are also presented .
figures  <dig> and  <dig> provide an overview of the consensus protein architectures of most of the elrr proteins, arranged in subfamilies.
a large number of singleton lrr_only proteins are not shown in this diagram .
list of singleton proteins in lrr_only group not shown in figure  <dig>  for the mammalian proteins, only the mouse orthologue is listed, with the following exceptions: both human and mouse nyctalopin  are listed as they have different topologies  and synleurin is a human gene that has been pseudogenised in mouse.
the elrr superfamily
we categorized the elrr proteins into four classes, based on their architecture and clustering.
these are lrr_ig/fn <dig> , lrr_tollkin , lrr_other  and lrr_only .
these categories are broadly supported by the clustering results, although the lrr_other group is clearly arbitrary and contains a number of unrelated subfamilies.
the number of elrr proteins in each of these classes in each of the four organisms studied is shown in table  <dig>  these are broken down into several categories, based on predicted localization: secreted, gpi-linked, type i transmembrane and multi-membrane spanning .
almost all of the lrr_ig/fn <dig> group are associated with the plasma membrane, either as type i tm or gpi-linked proteins.
in contrast, the lrr_tollkin and lrr_only groups contain a far higher percentage of secreted proteins.
it is clear from an examination of these data that the elrr superfamily has greatly expanded in mammals  and to a lesser extent, flies , compared to worms .
the numbers of elrr proteins in each of the four major groups is listed for each species, broken down by predicted protein localisation or topology: type i transmembrane, gpi-linked, secreted and multiple-membrane-spanning.
*includes cg <dig>  unclassified localisation.
subfamily expansion and diversification
in order to assess the extent of expansion  and diversification  across different organisms, we analysed the membership of clusters across mouse and fly.
for this purpose, we defined clusters in such a way as to distinguish those with species-specific expansion from those with diversification .
for each cluster we counted the number of fly and mouse members and then generated histograms of the number of clusters with x fly members and y mouse members .
for example, in the lrr_ig/fn <dig> group there is one cluster with one fly gene and three mouse genes  and there are six clusters with no fly genes and three mouse genes .
these graphs illustrate the different rates of expansion and diversification across these groups.
for the lrr_ig-fn <dig> family there is a large number of clusters that have multiple mouse genes and no fly genes.
these represent the diversification of new architectures and gene families in the mammalian lineage.
there is only one case of expansion in the mouse within a conserved subfamily ).
conversely, the kekkon family shows a specific expansion in flies compared to mammals, where there is only a single apparent closest orthologue, lrrc <dig>
in contrast, in the lrr_tollkin group there has been independent expansion of subfamilies in both flies and mammals .
similar expansions are observed in the subfamilies of toll-like receptor genes themselves and in the subfamilies of gene encoding proteins that do not have tir domains but that cluster within this group .
in the lrr_only group there has also been independent expansion, apparently followed by rapid divergence, resulting in a very large number of singletons in each species.
these are genes with no recognizable orthologue in the other species  and no recognizable paralogue in their own species.
the encoded proteins do not cluster at high stringency  but many cluster into a very large group at lower stringency.
this trend may reflect increased divergence rates of this class of proteins.
there is only one case in this group of apparent orthology, between cg <dig> in fly and tpbg/5t <dig>  <cit>  and a novel gene in mouse.
the lrr_other group shows the opposite pattern with the largest number of clear orthologues between mouse and fly .
this group also contains the most clusters with a clear worm orthologue .
the members of this group include the slit proteins, peroxidasins, and a number of g-protein coupled hormone receptors, which are all conserved, as well as a number of mammal-specific families including the lgi proteins.
clustering of known proteins
these analyses provide an overview of relationships within the elrr superfamily and highlight a number of previously unreported associations, allowing us to classify several novel proteins as paralogues of lrrc21/pal, tpbg/5t <dig>  lrrc <dig> or chad, for example.
conversely, it is clear that the recently named nlrr <dig> is not in fact a paralogue of the other nlrr proteins .
also, the lrig proteins in mammals are orthologous not to kekkon proteins in the fly, as has been suggested previously  <cit> , but to the lambik protein in flies .
the mammalian protein lrrc <dig> appears to be the closest orthologue of the kekkon proteins.
a particularly interesting finding is of a number of lrr proteins which cluster with the toll-like receptors in both flies and mammals but which do not have a characteristic tir domain.
one of these: cd <dig>  also known as rp <dig>  clusters specifically with tlr <dig>  this protein lacks a tir domain and has recently been found to act as a negative regulator of tlr <dig>  <cit> .
also in the lrr_tollkin group in mammals is a subgroup of more distantly related proteins: carboxypeptidase n subunit  <dig> , glycoprotein v  and leucine-rich repeat-containing protein  <dig> , , as well as insulin-growth factor acid labile subunit  and kiaa <dig> .
lrrc <dig>  and the related protein lrrc <dig> also fall into this cluster, along with the novel fly protein cg <dig>  in the fly there is also another subcluster that clusters with the tlrs.
this subcluster includes chaoptin, which is known to function as an adhesion molecule in neural development  <cit>  and several other novel proteins, including one with an fn <dig> domain .
the chaoptin cluster also contains the worm tol- <dig> protein and the novel worm protein c56e <dig> .
the large lrr_tollkin group is one example where the expectation of hierarchical clustering does not hold; in many cases, individual proteins in this broad family cluster into different subfamilies at different e-values and inflation parameters .
novel protein families
these analyses have also catalogued a large number of novel proteins and subfamilies encoding elrr proteins in worms, flies and mammals.
two novel clusters in mammals are of special interest due to their expression patterns in the nervous system .
one includes two closely related tm proteins currently identified as a930017n06rik and lrrc <dig> in the mouse.
these proteins form a distinct subfamily at high stringency and are characterized by a signal peptide,  <dig> lrr repeats, an lrr-ct and an fn <dig> domain extracellularly, a tm domain and a long cytoplasmic tail .
the cytoplasmic tail contains a large number of tyrosines but no other detectable motifs.
both genes have two exons with the coding sequence entirely in the 3' exon.
we propose to name these elfn proteins, for extracellular-leucine-rich repeat fibronectin domain proteins.
.
another cluster of related proteins comprises bc <dig> , lrrc <dig>  lrrc <dig> and lrrc <dig>  lrtm <dig> and lrtm <dig> .
these are all lrr_only proteins with a signal peptide, an lrr-nt,  <dig> lrr repeats, an lrr-ct, a tm domain and a short cytoplasmic tail containing a short stretch of acidic residues .
lrtm <dig> and  <dig> also contain conserved predicted pdz-binding sequences at their c-termini, suggestive of synaptic localisation.
these proteins cluster in a group of six at low stringency , but break into several subclusters at higher stringency .
they are defined as paralogues in the ensembl database but whether they represent a true "subfamily" is unclear.
for convenience, we refer to this cluster as the "elron" cluster, for extracellular-leucine-rich repeat-only proteins, but have not renamed individual members.
in flies there are several subfamilies of novel proteins.
these include cg <dig> and cg <dig>  both lrr_only tm proteins, cg <dig> and cg <dig>  both secreted lrr_only proteins and cg <dig> and cg <dig>  which cluster as a pair at e- <dig>  level  <dig> and in a group of four with cg <dig> and cg <dig> at e- <dig>  level  <dig>  cg <dig> is a predicted tm protein while the others in this cluster are predicted secreted proteins.
in worms, a subfamily emerges comprising sym- <dig>  and sym- <dig> , both of which interact genetically with mec- <dig> <cit> , along with c44h <dig>  and two other predicted proteins k07a <dig>  and zk <dig> .
there are also several cases of apparent one-to-one worm-fly orthology of novel proteins, including cg <dig> and zc <dig> a, cg <dig> and m <dig> a, cg <dig> and t01g <dig>  and cg <dig> and k07a <dig> .
genomic clustering
to assess the possibility that some related genes might occur in clusters in the genome we examined genomic locations for all genes in our elrr dataset .
not surprisingly, many closely related genes occur in tandem: five of the six slitrk genes occur in two clusters in the mouse, one on the x chromosome and one on chromosome  <dig>  other genes occurring in tandem include tlr <dig> and tlr <dig>  islr <dig> and islr <dig>  lrrc21/pal and lrrc <dig>  lrrc8b, c and d and fshr and lhcgr.
we also found a number of examples where more distantly related genes occur in tandem in the genome, lending further support to the clustering results presented above, including cpn <dig>  gp <dig> and lrrc15/lib.
in the fly, several toll-related genes occur in adjacent pairs , as do tartan and capricious.
similarly, a number of the novel subfamilies identified above occur in tandem including cg <dig> with cg <dig>  cg <dig> with cg <dig> and cg <dig> with cg <dig>  in the worm genome, the c44h <dig> genes  and .
<dig> ) also occur in tandem.
we observed an interesting situation in the family of small secreted proteoglycans that includes decorin, biglycan, and related genes.
proteins in this family fall into several subclusters using tribemcl, in agreement with previous analyses  <cit> .
interestingly, many of them are also grouped in tandem in the genome in several different loci but each locus contains a representative of two or three subclusters.
this suggests two early duplications in tandem and a subsequent triplication of the entire locus, with some additional gene losses and duplications  <cit> .
the ecm <dig> gene is also located in tandem in one of these loci  but it is highly divergent from the other proteins and whether it should be considered a member of this family is debatable  <cit> .
expression analyses
in order to begin to assess the possible involvement of these novel genes and families in neural development we analysed the expression of a subset of them by in situ hybridisation in the mouse or fly developing nervous system.
elfn <dig> and elfn <dig> show rather complementary expression patterns in the embryonic and postnatal mouse brain .
elfn <dig> is strongly expressed in interneurons in the hippocampus and cortex while elfn <dig> is expressed more broadly in the cortex in presumed glutamatergic neurons and in the hippocampus in pyramidal and granule cells.
in the basal ganglia, elfn <dig> is expressed in the globus pallidus, while elfn <dig> is expressed more strongly in the other major division, the striatum.
these patterns are maintained in adults, according to the allen brain atlas  <cit> .
based on abundance of cdnas in the unigene database, it appears that expression of elfn <dig> is quite restricted to the nervous system , while elfn <dig> is also expressed in endocrine and reproductive tissues .
among the genes in the elron cluster, three  are expressed in discrete regions of the developing mouse brain, in particular marking different nuclei in the developing thalamus as well as a number of other areas .
according to the allen brain atlas, the expression of lrtm <dig> declines after development and is practically undetectable in adults.
in contrast, lrtm <dig> is maintained at high levels in adults in a number of discrete regions including the granule cell layer in the olfactory bulb, the basal ganglia, dorsal thalamus, dentate gyrus, layers 2/ <dig> and  <dig> in the cortex and purkinje cells in the cerebellum.
lrrc <dig> is also maintained at high levels in mitral cells in the olfactory bulb, in the habenula and in layers  <dig> and 6a in the cortex.
lrrc <dig> is expressed at lower levels during development  but is expressed in a specific pattern in the adult brain, including the ca <dig> region of the hippocampus and the zona incerta  <cit> .
bc <dig> and lrrc <dig> do not appear to be expressed in the embryonic or postnatal brain.
this result is confirmed by the absence of expression in the allen brain atlas and by analysis of cdna abundance in the unigene database, which show that lrrc <dig>  is specific to muscle and testis and that bc <dig>  is almost exclusively expressed by the vesicular organ in the male reproductive system.
similar cdna abundance data for the other four genes show that lrtm <dig>  is almost brain-specific, while lrtm <dig>  lrrc <dig>  and lrrc <dig>  are also expressed in a small number of other tissues.
the expression of the elfn genes and of several genes in the elron cluster is thus consistent with a possible role in specifying neuronal connectivity, especially thalamic and cortical connectivity.
the expression patterns of many of the drosophila elrr genes identified in the bioinformatic screen were also examined in the embryo by in situ hybridisation.
a summary of the expression patterns we identified and those previously described is presented .
we describe here the expression patterns of those novel elrr genes identified in our survey that include expression in the nervous system .
cg <dig> is expressed dynamically in the peripheral nervous system , appearing at stage  <dig> and disappearing during stage  <dig>  cg <dig> is exclusively expressed in the cns and is restricted to a subset of cells at the ventral midline, beginning during stage  <dig> and remaining into stage  <dig>  cg <dig> expression is restricted to the most dorsal layer of the cns in a position consistent with the longitudinal glia.
this expression begins at stage  <dig> and continues throughout embryonic development.
cg <dig> is expressed from stage  <dig> throughout the embryo with exception of the anterior tip .
at stage  <dig> expression of cg <dig> is initiated in a subset of cells in the cns.
cg <dig> is expressed in an anteroposterior stripe within the neurogenic region and in the prospective brain lobes during stages 8– <dig>  and in discrete cells at the midline of the cns during stages  <dig> and  <dig>  from stage  <dig> onwards cg <dig> expression is seen predominantly in the somatic musculature.
discussion
this study aimed to catalogue the full repertoire of elrr proteins in the proteomes of worms, flies, mice and humans, to examine their evolutionary relationships and to identify novel proteins and subfamilies that may have important roles in nervous system development.
methodological issues
generating this dataset required identifying all lrr proteins, distinguishing elrr proteins among this set  and analyzing evolutionary relationships across a large set of highly divergent, multi-domain, repetitive proteins in four distantly related species.
for all of these tasks we found the use of single programs only partly reliable.
it was especially difficult to derive a single set of parameters for any program that would reliably predict the presence of a particular motif or correctly identify orthologues and paralogues for all proteins in the dataset.
to overcome this problem we developed an approach of parallel annotation with many different programs, followed by manual curation to arrive at a consensus architecture for each gene, along with a hierarchical clustering method designed to reveal relationships at multiple levels.
this is in contrast to the automated one-size-fits-all approaches currently used by some of the large genome databases.
a comprehensive, curated dataset
our bioinformatics searches and exhaustive manual curation have yielded what we are confident should be an extremely comprehensive set of elrr proteins across the four species examined.
rather than employing a series of strict filters we used the combined evidence from a variety of prediction programs and from clustering to distinguish elrr proteins from intracellular lrr proteins.
we think it is therefore unlikely that we have missed many true elrr genes in any of the organisms.
this obviously depends however on the quality and comprehensiveness of the gene predictions in our starting datasets.
there may in the first instance be cases of genes that have simply not been predicted at all yet.
we also came across numerous cases of mispredicted genes where only a fragment was predicted or where a single orf was split into two predicted genes, for example.
in most of these cases the fragments still clustered with other elrr genes and a full-length sequence was often identifiable from one of the starting datasets.
it is difficult to estimate how common such annotation errors are but it is reasonable to expect that they may have caused us to miss a small number of additional elrr genes or to misclassify some as cytoplasmic.
the manual curation of these sequences has added substantially to the value of this dataset.
an appreciable percentage of predicted protein sequences had to be amended in some way to yield what we consider to be the "correct" predicted full-length protein.
these corrections were based on various factors including comparison of architectures across orthologues or paralogues, the absence of an expected signal peptide or the location of the predicted start codon with respect to the signal peptide.
because degenerate or atypical lrrs have been described  <cit>  that do not match the consensus motifs defined by smart and pfam we designed a customised program, lrrscan, to search for a minimal consensus that defines animal extracellular lrrs.
we also searched for minimal consensus motifs that define lrr-nt and several varieties of lrr-ct domains found in different types of proteins, including small proteoglycans and g-protein-coupled receptors  <cit> .
these predictions were compared with the results of smart and pfam  and a consensus architecture was predicted by manual inspection, based on converging evidence.
the results match those of proteins with known structures  <cit> , significantly better than a combination of smart and pfam alone .
the predicted transmembrane topologies are also based on converging evidence from multiple programs and have also been subject to expert evaluation.
nevertheless, the architectures presented should be viewed as predictions that will require experimental verification.
in particular, the absence of a predicted lrr-nt or lrr-ct domain does not mean there is no domain present that is performing a capping function; there may be additional varieties of such domains that have not yet been defined.
in addition, we have chosen a representative isoform for each gene; the database thus contains no information on alternative splice forms or other isoforms that may have differing architectures.
hierarchical clustering
the hierarchical clustering method we used gets around the problem of defining a unique set of parameters that is suitable to all proteins and levels of inter-relationship.
in most cases, it generates a tree-like structure that reveals relationships across many different levels at once.
this is a difficult problem for multiple alignment programs such as clustalw or t-coffee, which work well for closely related proteins but which are not designed to compare highly divergent proteins with differing architectures.
previous attempts using multiple alignment programs to derive a phylogenetic tree across many elrr subfamilies at once contain numerous differences from our results and from known relationships  <cit> .
in some cases, the results of tribemcl depart from the expected hierarchical relationship.
this is the case for the extended lrr_tollkin group of proteins, including many proteins characterized by an elrr domain but lacking an obvious tir domain.
while the clustering of these proteins with the tlr group is quite convincing, based on direct inspection of the blast results, it is extremely difficult, indeed impossible with these data, to discern more discrete relationships within this large family.
the reasons for the anomalous hierarchical clustering results with these genes may relate to the large number of lrrs present in these proteins and the very slight differences in pairwise similarities across the group.
at different levels of stringency small differences in blast scores may be amplified by the tribemcl algorithm to result in membership of different clusters that do not share the expected hierarchical relationship.
attempts to resolve the phylogeny of all the genes in this group using the t-coffee multiple alignment program were no more enlightening, resulting in a starburst pattern where the roots of each subfamily are too close to each other to resolve .
despite these limitations, the tribemcl analysis has revealed a group of elrr proteins that are clearly more related to the tlr proteins than to other lrr_only proteins.
nomenclature
the current nomenclature of elrr proteins is very confusing, with multiple synonyms for many genes , many of which do not give accurate information on relationships.
for example, there is a large number of proteins designated lrrcx, where x is a number.
these names were apparently derived from large-scale genome projects and do not represent a specific subfamily of related proteins.
for that reason we have proposed the names elfn <dig> and  <dig> for one novel subfamily.
we also identify another discrete cluster of six "novel" proteins , although whether they represent a true subfamily is open to debate.
in addition, some novel proteins that group into small subfamilies with lrrc21/pal, chad, lrrc <dig> and tpbg/5t <dig>  could be given names to reflect that fact.
finally, while lrrn <dig>   <dig> and  <dig>  form a subfamily, the recently named nlrr <dig>  <cit>  is not in fact a member of this subfamily.
it does not have the ig domain present in these genes and does not cluster with those genes at any parameters.
comparative analyses of major groups
for the purposes of some of the analyses we split the elrr proteins into four groups, based on architecture and clustering results.
the lrr_ig/fn <dig> group includes the largest percentage of mammal-specific subfamilies, many with multiple members.
almost all of the proteins in this group are associated with the membrane, either type i tm or gpi-linked.
the majority of these subfamilies  show discrete expression in the nervous system and many of them have been shown to have functions in neural development  <cit>  and/or have been implicated in neurological or psychiatric disease  <cit> .
expansion of this class of proteins is thus correlated with the evolution of the complex mammalian brain and plausibly contributed to it by providing the requisite specificity of cellular interactions to mediate a large number of selective connectivity decisions.
we have identified a novel mammal-specific lrr_fn <dig> subfamily, the elfn proteins, with discrete nervous system expression.
the lrr_only group shows independent diversification in flies and mammals, with a large number of singletons , suggesting rapid sequence divergence.
this group also contains a number of proteins implicated in nervous system development or function including the nogo-receptor, lrrtm and slitrk families, and connectin, gp <dig>  tpbg/5t <dig> and nyx for example.
we have identified six proteins in another novel mammal-specific cluster, the elron cluster, several of which show highly suggestive expression patterns in the developing nervous system.
we have also discovered a number of novel fly proteins in this class that are similarly discretely expressed in the embryonic nervous system.
the lrr_tollkin group shows a different pattern of evolution, with parallel expansions in flies and mammals, of both the toll-like receptor genes and of the genes that cluster with them.
in mammals, the latter include cpn <dig>  gp <dig> and lrrc <dig> , which form a subcluster and which are also arrayed in tandem on chromosome  <dig> in humans .
these proteins have diverse binding partners and biochemical functions but are all involved in inflammation in some way: as a regulatory subunit of carboxypeptidase  <cit> , as a component of the platelet glycoprotein complex , and as a mediator of the glial response to β-amyloid  <cit> , respectively.
they form a slightly larger cluster with igfals, the acid-labile subunit of insulin growth factor, which regulates igf signaling  <cit>  and with the novel gene kiaa <dig>  which has an fn <dig> domain in addition to the lrr domain.
lrrc32/garp  <cit>  and its paralogue lrrc <dig> also cluster with this group at e- <dig>  level  <dig>  but not at some lower levels.
the novel fly gene cg <dig> also clusters with this group at e- <dig>  level  <dig> and with other fly genes including chaoptin at some other levels.
whether it can be said to be directly orthologous to any  of these mammalian proteins is hard to determine.
the other fly genes in this group are mostly novel and include cg40500-pd, which has an fn <dig> domain and which shows very discrete expression in the midline of the embryonic nervous system.
the lrr_other group is an arbitrary default group as it contains many unrelated genes or subfamilies.
nevertheless, it is interesting to note that this group contains the highest percentage of genes with orthologues across all species, including worms .
this group also includes the mammalian lgi subfamily, recently implicated in epilepsy and myelination.
human-mouse differences
only a small number of proteins are specific to either human or mouse.
there are two cases where there are genes in humans that are not represented in mouse that both seem to be caused by specific loss in mice, rather than representing human-specific genes.
the human gene synleurin appears to have been pseudogenised in rodents, although it is present in many other species besides humans .
also, mxra <dig> , a paralogue of the large secreted protein igsf <dig>  is not detectable in the mouse genome .
there is also a small number of examples where there has been independent expansion of subfamilies in either humans or mice.
these include the toll-like receptors tlr <dig> in humans and tlr <dig>   <dig> and  <dig> in the mouse.
they also include the unusual subfamily of lrrc <dig> genes, which is represented by a single gene in the mouse  but multiple, highly related genes in humans .
these are located in tandem on chromosome  <dig> and have arisen from multiple duplications of the brca <dig> region in primates  <cit> .
the extracellular domains of these tm proteins are characterised by six predicted lrrs but these make up only a small fraction of the overall protein, which is highly variable in length.
the functions and expression patterns of these unusual proteins are unknown.
CONCLUSIONS
this survey presents a comprehensive overview of the repertoire of elrr proteins in various species and their inter-relationships.
as such, it provides the necessary foundation for a systematic analysis of the functions of this class of genes, which are likely to include prominently neural development, innate immunity and inflammation.
in particular, expansion of the elrr proteome is correlated with increasing complexity of the nervous system.
given the functions and discrete expression patterns of many known members, it seems likely that this superfamily, including the novel proteins identified here, could provide the requisite specificity of cellular interactions to mediate a large number of selective connectivity decisions.

 leucine-rich repeats are protein-ligand interaction motifs found in a large number of proteins of diverse structure, localization and function in bacteria, fungi, plants and animals. many of these have well-known functions in the innate immune system. many others, especially those with extracellular lrrs, are involved in various aspects of nervous system development. in both cases, the nature of the lrr motifs is important for generating a diversity of interactions, with exogenous factors in the immune system and with the huge number of different cell types in the developing nervous system. the structure of lrr motifs and their arrangement in repetitive stretches of variable length generate a versatile and highly evolvable framework for the binding of diverse proteins and non-protein ligands. seven classes of lrr have been defined;. within animals, four separate types are recognised, three typically intracellular and one extracellular. whether all these different classes are evolutionarily related by descent or represent convergent evolution is open to debate but they all share a characteristic structure. each repeat is typically 19– amino acids long and has a well-conserved n-terminal stretch of 9– amino acids that is characterized by precisely-positioned hydrophobic residues and that forms a β-strand and a c-terminal stretch of 10– amino acids that is more variable in length, sequence and structure. the arrangement of multiple repeats in tandem generates a horseshoe-shaped solenoidal structure, with the β-strands stacking to form the concave surface and the variable stretches forming the convex surface. most lrr regions typically also have both n-terminal and c-terminal cap regions, which shield the hydrophobic core of the lrr structure. in extracellular proteins these regions are defined by precisely positioned cysteine residues. lrr proteins, both intracellular and extracellular, have well-characterized functions in the innate immune system that are similar from plants to mammals. the extracellular lrr proteins in animals include the toll-like receptors, a family of transmembrane proteins characterized by an lrr region, a transmembrane domain and a cytoplasmic toll/il- receptor domain. this family has expanded in vertebrates to allow detection of a diverse set of antigens. in flies, the tlr family has also expanded, where, in addition to roles in immunity for some of these proteins, many are required for various aspects of embryonic and nervous system development. tol- in worms is also important in development, possibly contributing to a code of molecules defining neuronal connectivity. recent reports indicate that some mammalian tlr genes may also be expressed and function in neurons. a large number of other elrr proteins have been implicated in various aspects of neural development, genetically in flies and in mammals in assays of neurite outgrowth,, fasciculation and/or synapse formation. some of these contain, in addition to the extracellular lrr domain, immunoglobulin or fibronectin type- domains. in some cases, the functions of elrr proteins are mediated by homophilic interactions. in other cases they are mediated by the binding of other proteins in cis and in trans. several elrr proteins have been found to modulate the signaling of various growth factor pathways. surprisingly, apart from the tlr genes and small secreted proteoglycans, relatively few elrr genes have been studied genetically in mice. among the ones that have, examples of phenotypic effects in the nervous system include increased plasticity, sprouting and nerve regeneration, and defects in axon guidance and cell migration, learning and memory, myelination and neuronal survival. the importance of this class of proteins for nervous system development in humans is apparent from the large number of examples implicated in neurological or psychiatric disorders. these include epilepsy, tourette's syndrome, night blindness, congenital insensitivity to pain, and possible links to alzheimer's disease. despite the growing number of elrr proteins implicated in nervous system development or disease this family of proteins has received far less attention as a class than other better characterized families like the immunoglobulin and cadherin superfamilies. in particular, there have been no systematic surveys of the genomic complement of these proteins or investigation of their evolutionary relationships. we therefore set out to catalogue the entire extracellular leucine-rich repeat proteome of four organisms: caenorhabditis elegans, drosophila melanogaster, mus musculus and homo sapiens. we used a hierarchical clustering system to analyse within and between-species relationships, revealing independent diversification and expansion of subfamilies in each species and rapid sequence divergence. these analyses highlight the large number of novel, uncharacterized elrr proteins in each of these genomes, including several novel subfamilies. a number of these show highly restricted expression in the nervous system in mouse or fly.  bioinformatics strategy we began by obtaining whole proteome sets of known and predicted proteins from each of the four species, as described in methods. our initial approach was to filter the starting proteome datasets using transmembrane and signal peptide prediction programs to identify transmembrane or secreted proteins and then to filter that set using a motif recognition program to identify the subset with motifs of interest. however, this approach using serial filters missed a number of known axon guidance molecules because tmhmm or signalp misclassified them or pfam did not detect specific motifs. we therefore included a number of other tm-prediction and motif-recognition programs, including a customised program to look for lrr domains in the pipeline. in addition, we first performed a clustering step using tribemcl on the entire proteome sets so as to identify related proteins even where these programs failed to detect specific motifs or architectures. rather than using any of these programs or a combination of them as strict filters we generated a database containing all the results that could be browsed or searched using various criteria to extract particular gene families of interest. clustering tribemcl is a program designed to cluster proteins into related families based on simultaneous examination of all pairwise blast similarity scores. this program uses a markov cluster algorithm that is particularly well suited to cluster highly divergent proteins with repeated domains into separate subfamilies, a task for which multiple alignment programs are not appropriate. the markov cluster algorithm is based on simulated'flow' through a network or graph, where each node is a gene and each connection is weighted by the symmetric pairwise blast score. a random walk of a certain length from node to node through this network, which begins within a small cluster of interconnected genes will have a higher probability of ending up within that cluster than crossing to a gene that is only related to one of them. the results of many simulated random walks thus define the clusters. each time this process is reiterated the links within the clusters that emerge are given a higher weighting and the links that were not used are downgraded. multiple rounds of this process eventually lead to completely separate clusters. there are two parameters that can be varied that affect the clustering; the first is the e-value cutoff of the blast scores that are used, which determines the initial set of connections considered. the second is the inflation parameter; this determines how strongly the links are upgraded with each iteration. a higher inflation parameter increases the'granularity' of the output; i.e., it generates a larger number of smaller clusters. we used a number of e-value cutoffs and inflation parameters and compared the output produced. the output from tribemcl, for any given e-value cutoff and inflation parameter can be viewed as a list of genes organized into clusters with a number assigned to each cluster. we reasoned that hierarchical sorting of genes across various inflation parameters should yield a tree-like structure, with larger clusters at low inflation parameters splitting into more discrete clusters at higher inflation parameters. at each e-value cutoff we therefore sorted the list of genes first at inflation parameter, then then and for the most part, increasing inflation parameter does lead to splitting of large clusters into smaller clusters and yields a tree-like arrangement of genes with relationships apparent across various levels. identification of lrr motifs to identify lrr proteins, the database was searched for all genes containing at least one lrr, lrr-nt or lrr-ct predicted by either smart or pfam. the cutoff values used were based on analysis of the results for proteins with known architecture. this analysis yielded a total of entries. these include both genes with intracellular lrrs and those with extracellular lrrs. it also contains isoforms for many genes. to screen out false positives we used the following criteria: if only one lrr was predicted in a gene and only by one of the programs and it was not predicted in either the mouse or human orthologue, or in other members of a closely-related cluster then it was considered a false positive and discarded. identification of extracellular lrr proteins comparison of several tm-prediction programs suggests that tmhmm is the most reliable, although it is also the most selective. a quick survey of some known tm receptors revealed that tmhmm failed to identify tm domains in several of them, including robo in mammals and kekkon and in flies, for example. for that reason we also used two other programs, hmmtop and tmpred to search for tm domains. at least one of these three programs successfully detected the tm domain in all the known tm receptors examined. signalp was used to detect signal peptides. this suffered from poor prediction of 5' exons for many mammalian genes, which was solved by manual curation. the gpi-prediction program big-pi identified a small number of gpi-linked proteins, including all the known gpi-linked proteins such as connectin, ngrs and nyx. a number of other genes were tentatively assigned to the gpi-class by manual inspection based on the presence of a characteristic short c-terminal hydrophobic stretch. in addition to examining the convergent evidence from these various programs to identify elrr proteins we used three additional criteria. the first is the type of lrr predicted: extracellular proteins typically contain lrr types designated lrr_ or by pfam or lrr_typical by smart, while the intracellular proteins have lrr_ri or lrr_sd. second, the prediction of an lrr-nt and/or lrr-ct domain was taken as evidence for extracellular localization. third, especially at low e-value stringencies, the majority of extracellular lrr proteins cluster together with tribemcl in one large group, distinct from the intracellular proteins. using these criteria in addition to the data from the prediction programs described above we collected what we believe is a comprehensive set of extracellular lrr proteins across worm, fly, mouse and human. we call these the elrr proteome. manual curation of extracellular sequences to reduce the complexity of the final data set a single protein isoform was chosen for each gene and all others were removed. many peptides that we expected to be extracellular because of orthology, clustering or domain structure did not have a predicted signal peptide. upon manual inspection of the sequences it was discovered that many gene predictions in ensembl, especially for mammalian genes, were missing the 5'-most exon encoding the signal peptide. for many such genes we identified the 5' exon and the full coding sequence in a sequence from another database and/or by searching with an orthologous gene from mouse or human. in other cases the 5' predicted sequence extended past the apparent true methionine start codon, which could be recognised by conservation and the presence of the signal sequence. we identified two fly genes that have been incorrectly annotated in ensembl as two separate genes each. cg and cg represent the 5' and 3' ends of a cdna encoded by ab a new member of the lgr family. similarly, cg and cg represent the 5' and 3' ends of the fish-lips gene, encoded by aav which is related to tartan and capricious. we detected one similar mis-annotation in the worm database and presume that this type of error may also have occurred for some mammalian sequences. all the manually curated gene sequences are provided. these curated sequences were fed back into the starting database and the blast and clustering analyses were re-performed to ensure that spurious results had not been generated by incorrect sequences. defining consensus architectures in order to derive a consensus architecture for each gene we compared the results of smart and pfam and the tm-prediction programs. even at very low stringency some lrrs in proteins with known numbers of such repeats were missed by hmmpfam using the smart and pfam databases. this includes a number of somewhat degenerate lrrs in lrrc proteins, for example, as well as atypical lrr-ct domains in small proteoglycans and g-protein-coupled receptors. for this reason, using a similar strategy to smits and colleagues, we wrote a customised program, lrrscan, to search for a more inclusive minimal consensus that defines lrrs as well as searching for consensus sequences derived from non-canonical lrr-ct domains. lrrscan was successful in identifying all the predicted lrrs in lrrc proteins, including atypical or degenerate ones, and additional lrrs in many other proteins that were not detected by hmmpfam with smart or pfam. the output from lrrscan and hmmpfam was compared for all proteins and a consensus architecture including number of lrrs and presence of lrr-nt and lrr-ct domains was derived by manual curation. the consensus matches the architecture of a number of elrr proteins with published structures, allowing for semantic differences in how the lrrs are counted. the final lrr before the lrr-ct domain often contains only the first subdomain of nine residues; following the convention of matsushima and colleagues we count this as one repeat rather than part of the lrr-ct domain, which in some cases may cause an apparent discrepancy with published reports. we also do not count in the total number of repeats putative lrrs which overlap with lrr-nt or lrr-ct domains, as has been done in some published cases. a consensus topology for each protein was also derived by comparison of the signal peptide, gpi anchor and tm-prediction programs. a full list of all the elrr proteins is provided and a sample is shown in figure these are sorted hierarchically across inflation parameters at an e-value cutoff of - clustering results at e- and e- are also presented. figures and provide an overview of the consensus protein architectures of most of the elrr proteins, arranged in subfamilies. a large number of singleton lrr_only proteins are not shown in this diagram. list of singleton proteins in lrr_only group not shown in figure for the mammalian proteins, only the mouse orthologue is listed, with the following exceptions: both human and mouse nyctalopin are listed as they have different topologies and synleurin is a human gene that has been pseudogenised in mouse. the elrr superfamily we categorized the elrr proteins into four classes, based on their architecture and clustering. these are lrr_ig/fn, lrr_tollkin, lrr_other and lrr_only. these categories are broadly supported by the clustering results, although the lrr_other group is clearly arbitrary and contains a number of unrelated subfamilies. the number of elrr proteins in each of these classes in each of the four organisms studied is shown in table these are broken down into several categories, based on predicted localization: secreted, gpi-linked, type i transmembrane and multi-membrane spanning. almost all of the lrr_ig/fn group are associated with the plasma membrane, either as type i tm or gpi-linked proteins. in contrast, the lrr_tollkin and lrr_only groups contain a far higher percentage of secreted proteins. it is clear from an examination of these data that the elrr superfamily has greatly expanded in mammals and to a lesser extent, flies, compared to worms. the numbers of elrr proteins in each of the four major groups is listed for each species, broken down by predicted protein localisation or topology: type i transmembrane, gpi-linked, secreted and multiple-membrane-spanning. *includes cg unclassified localisation. subfamily expansion and diversification in order to assess the extent of expansion and diversification across different organisms, we analysed the membership of clusters across mouse and fly. for this purpose, we defined clusters in such a way as to distinguish those with species-specific expansion from those with diversification. for each cluster we counted the number of fly and mouse members and then generated histograms of the number of clusters with x fly members and y mouse members. for example, in the lrr_ig/fn group there is one cluster with one fly gene and three mouse genes and there are six clusters with no fly genes and three mouse genes. these graphs illustrate the different rates of expansion and diversification across these groups. for the lrr_ig-fn family there is a large number of clusters that have multiple mouse genes and no fly genes. these represent the diversification of new architectures and gene families in the mammalian lineage. there is only one case of expansion in the mouse within a conserved subfamily ). conversely, the kekkon family shows a specific expansion in flies compared to mammals, where there is only a single apparent closest orthologue, lrrc in contrast, in the lrr_tollkin group there has been independent expansion of subfamilies in both flies and mammals. similar expansions are observed in the subfamilies of toll-like receptor genes themselves and in the subfamilies of gene encoding proteins that do not have tir domains but that cluster within this group. in the lrr_only group there has also been independent expansion, apparently followed by rapid divergence, resulting in a very large number of singletons in each species. these are genes with no recognizable orthologue in the other species and no recognizable paralogue in their own species. the encoded proteins do not cluster at high stringency but many cluster into a very large group at lower stringency. this trend may reflect increased divergence rates of this class of proteins. there is only one case in this group of apparent orthology, between cg in fly and tpbg/5t and a novel gene in mouse. the lrr_other group shows the opposite pattern with the largest number of clear orthologues between mouse and fly. this group also contains the most clusters with a clear worm orthologue. the members of this group include the slit proteins, peroxidasins, and a number of g-protein coupled hormone receptors, which are all conserved, as well as a number of mammal-specific families including the lgi proteins. clustering of known proteins these analyses provide an overview of relationships within the elrr superfamily and highlight a number of previously unreported associations, allowing us to classify several novel proteins as paralogues of lrrc21/pal, tpbg/5t lrrc or chad, for example. conversely, it is clear that the recently named nlrr is not in fact a paralogue of the other nlrr proteins. also, the lrig proteins in mammals are orthologous not to kekkon proteins in the fly, as has been suggested previously, but to the lambik protein in flies. the mammalian protein lrrc appears to be the closest orthologue of the kekkon proteins. a particularly interesting finding is of a number of lrr proteins which cluster with the toll-like receptors in both flies and mammals but which do not have a characteristic tir domain. one of these: cd also known as rp clusters specifically with tlr this protein lacks a tir domain and has recently been found to act as a negative regulator of tlr. also in the lrr_tollkin group in mammals is a subgroup of more distantly related proteins: carboxypeptidase n subunit, glycoprotein v and leucine-rich repeat-containing protein,, as well as insulin-growth factor acid labile subunit and kiaa. lrrc and the related protein lrrc also fall into this cluster, along with the novel fly protein cg in the fly there is also another subcluster that clusters with the tlrs. this subcluster includes chaoptin, which is known to function as an adhesion molecule in neural development and several other novel proteins, including one with an fn domain. the chaoptin cluster also contains the worm tol- protein and the novel worm protein c56e. the large lrr_tollkin group is one example where the expectation of hierarchical clustering does not hold; in many cases, individual proteins in this broad family cluster into different subfamilies at different e-values and inflation parameters. novel protein families these analyses have also catalogued a large number of novel proteins and subfamilies encoding elrr proteins in worms, flies and mammals. two novel clusters in mammals are of special interest due to their expression patterns in the nervous system. one includes two closely related tm proteins currently identified as a930017n06rik and lrrc in the mouse. these proteins form a distinct subfamily at high stringency and are characterized by a signal peptide, lrr repeats, an lrr-ct and an fn domain extracellularly, a tm domain and a long cytoplasmic tail. the cytoplasmic tail contains a large number of tyrosines but no other detectable motifs. both genes have two exons with the coding sequence entirely in the 3' exon. we propose to name these elfn proteins, for extracellular-leucine-rich repeat fibronectin domain proteins.. another cluster of related proteins comprises bc, lrrc lrrc and lrrc lrtm and lrtm. these are all lrr_only proteins with a signal peptide, an lrr-nt, lrr repeats, an lrr-ct, a tm domain and a short cytoplasmic tail containing a short stretch of acidic residues. lrtm and also contain conserved predicted pdz-binding sequences at their c-termini, suggestive of synaptic localisation. these proteins cluster in a group of six at low stringency, but break into several subclusters at higher stringency. they are defined as paralogues in the ensembl database but whether they represent a true "subfamily" is unclear. for convenience, we refer to this cluster as the "elron" cluster, for extracellular-leucine-rich repeat-only proteins, but have not renamed individual members. in flies there are several subfamilies of novel proteins. these include cg and cg both lrr_only tm proteins, cg and cg both secreted lrr_only proteins and cg and cg which cluster as a pair at e- level and in a group of four with cg and cg at e- level cg is a predicted tm protein while the others in this cluster are predicted secreted proteins. in worms, a subfamily emerges comprising sym- and sym-, both of which interact genetically with mec-, along with c44h and two other predicted proteins k07a and zk. there are also several cases of apparent one-to-one worm-fly orthology of novel proteins, including cg and zc a, cg and m a, cg and t01g and cg and k07a. genomic clustering to assess the possibility that some related genes might occur in clusters in the genome we examined genomic locations for all genes in our elrr dataset. not surprisingly, many closely related genes occur in tandem: five of the six slitrk genes occur in two clusters in the mouse, one on the x chromosome and one on chromosome other genes occurring in tandem include tlr and tlr islr and islr lrrc21/pal and lrrc lrrc8b, c and d and fshr and lhcgr. we also found a number of examples where more distantly related genes occur in tandem in the genome, lending further support to the clustering results presented above, including cpn gp and lrrc15/lib. in the fly, several toll-related genes occur in adjacent pairs, as do tartan and capricious. similarly, a number of the novel subfamilies identified above occur in tandem including cg with cg cg with cg and cg with cg in the worm genome, the c44h genes and. ) also occur in tandem. we observed an interesting situation in the family of small secreted proteoglycans that includes decorin, biglycan, and related genes. proteins in this family fall into several subclusters using tribemcl, in agreement with previous analyses. interestingly, many of them are also grouped in tandem in the genome in several different loci but each locus contains a representative of two or three subclusters. this suggests two early duplications in tandem and a subsequent triplication of the entire locus, with some additional gene losses and duplications. the ecm gene is also located in tandem in one of these loci but it is highly divergent from the other proteins and whether it should be considered a member of this family is debatable. expression analyses in order to begin to assess the possible involvement of these novel genes and families in neural development we analysed the expression of a subset of them by in situ hybridisation in the mouse or fly developing nervous system. elfn and elfn show rather complementary expression patterns in the embryonic and postnatal mouse brain. elfn is strongly expressed in interneurons in the hippocampus and cortex while elfn is expressed more broadly in the cortex in presumed glutamatergic neurons and in the hippocampus in pyramidal and granule cells. in the basal ganglia, elfn is expressed in the globus pallidus, while elfn is expressed more strongly in the other major division, the striatum. these patterns are maintained in adults, according to the allen brain atlas. based on abundance of cdnas in the unigene database, it appears that expression of elfn is quite restricted to the nervous system, while elfn is also expressed in endocrine and reproductive tissues. among the genes in the elron cluster, three are expressed in discrete regions of the developing mouse brain, in particular marking different nuclei in the developing thalamus as well as a number of other areas. according to the allen brain atlas, the expression of lrtm declines after development and is practically undetectable in adults. in contrast, lrtm is maintained at high levels in adults in a number of discrete regions including the granule cell layer in the olfactory bulb, the basal ganglia, dorsal thalamus, dentate gyrus, layers 2/ and in the cortex and purkinje cells in the cerebellum. lrrc is also maintained at high levels in mitral cells in the olfactory bulb, in the habenula and in layers and 6a in the cortex. lrrc is expressed at lower levels during development but is expressed in a specific pattern in the adult brain, including the ca region of the hippocampus and the zona incerta. bc and lrrc do not appear to be expressed in the embryonic or postnatal brain. this result is confirmed by the absence of expression in the allen brain atlas and by analysis of cdna abundance in the unigene database, which show that lrrc is specific to muscle and testis and that bc is almost exclusively expressed by the vesicular organ in the male reproductive system. similar cdna abundance data for the other four genes show that lrtm is almost brain-specific, while lrtm lrrc and lrrc are also expressed in a small number of other tissues. the expression of the elfn genes and of several genes in the elron cluster is thus consistent with a possible role in specifying neuronal connectivity, especially thalamic and cortical connectivity. the expression patterns of many of the drosophila elrr genes identified in the bioinformatic screen were also examined in the embryo by in situ hybridisation. a summary of the expression patterns we identified and those previously described is presented. we describe here the expression patterns of those novel elrr genes identified in our survey that include expression in the nervous system. cg is expressed dynamically in the peripheral nervous system, appearing at stage and disappearing during stage cg is exclusively expressed in the cns and is restricted to a subset of cells at the ventral midline, beginning during stage and remaining into stage cg expression is restricted to the most dorsal layer of the cns in a position consistent with the longitudinal glia. this expression begins at stage and continues throughout embryonic development. cg is expressed from stage throughout the embryo with exception of the anterior tip. at stage expression of cg is initiated in a subset of cells in the cns. cg is expressed in an anteroposterior stripe within the neurogenic region and in the prospective brain lobes during stages 8– and in discrete cells at the midline of the cns during stages and from stage onwards cg expression is seen predominantly in the somatic musculature. discussion this study aimed to catalogue the full repertoire of elrr proteins in the proteomes of worms, flies, mice and humans, to examine their evolutionary relationships and to identify novel proteins and subfamilies that may have important roles in nervous system development. methodological issues generating this dataset required identifying all lrr proteins, distinguishing elrr proteins among this set and analyzing evolutionary relationships across a large set of highly divergent, multi-domain, repetitive proteins in four distantly related species. for all of these tasks we found the use of single programs only partly reliable. it was especially difficult to derive a single set of parameters for any program that would reliably predict the presence of a particular motif or correctly identify orthologues and paralogues for all proteins in the dataset. to overcome this problem we developed an approach of parallel annotation with many different programs, followed by manual curation to arrive at a consensus architecture for each gene, along with a hierarchical clustering method designed to reveal relationships at multiple levels. this is in contrast to the automated one-size-fits-all approaches currently used by some of the large genome databases. a comprehensive, curated dataset our bioinformatics searches and exhaustive manual curation have yielded what we are confident should be an extremely comprehensive set of elrr proteins across the four species examined. rather than employing a series of strict filters we used the combined evidence from a variety of prediction programs and from clustering to distinguish elrr proteins from intracellular lrr proteins. we think it is therefore unlikely that we have missed many true elrr genes in any of the organisms. this obviously depends however on the quality and comprehensiveness of the gene predictions in our starting datasets. there may in the first instance be cases of genes that have simply not been predicted at all yet. we also came across numerous cases of mispredicted genes where only a fragment was predicted or where a single orf was split into two predicted genes, for example. in most of these cases the fragments still clustered with other elrr genes and a full-length sequence was often identifiable from one of the starting datasets. it is difficult to estimate how common such annotation errors are but it is reasonable to expect that they may have caused us to miss a small number of additional elrr genes or to misclassify some as cytoplasmic. the manual curation of these sequences has added substantially to the value of this dataset. an appreciable percentage of predicted protein sequences had to be amended in some way to yield what we consider to be the "correct" predicted full-length protein. these corrections were based on various factors including comparison of architectures across orthologues or paralogues, the absence of an expected signal peptide or the location of the predicted start codon with respect to the signal peptide. because degenerate or atypical lrrs have been described that do not match the consensus motifs defined by smart and pfam we designed a customised program, lrrscan, to search for a minimal consensus that defines animal extracellular lrrs. we also searched for minimal consensus motifs that define lrr-nt and several varieties of lrr-ct domains found in different types of proteins, including small proteoglycans and g-protein-coupled receptors. these predictions were compared with the results of smart and pfam and a consensus architecture was predicted by manual inspection, based on converging evidence. the results match those of proteins with known structures, significantly better than a combination of smart and pfam alone. the predicted transmembrane topologies are also based on converging evidence from multiple programs and have also been subject to expert evaluation. nevertheless, the architectures presented should be viewed as predictions that will require experimental verification. in particular, the absence of a predicted lrr-nt or lrr-ct domain does not mean there is no domain present that is performing a capping function; there may be additional varieties of such domains that have not yet been defined. in addition, we have chosen a representative isoform for each gene; the database thus contains no information on alternative splice forms or other isoforms that may have differing architectures. hierarchical clustering the hierarchical clustering method we used gets around the problem of defining a unique set of parameters that is suitable to all proteins and levels of inter-relationship. in most cases, it generates a tree-like structure that reveals relationships across many different levels at once. this is a difficult problem for multiple alignment programs such as clustalw or t-coffee, which work well for closely related proteins but which are not designed to compare highly divergent proteins with differing architectures. previous attempts using multiple alignment programs to derive a phylogenetic tree across many elrr subfamilies at once contain numerous differences from our results and from known relationships. in some cases, the results of tribemcl depart from the expected hierarchical relationship. this is the case for the extended lrr_tollkin group of proteins, including many proteins characterized by an elrr domain but lacking an obvious tir domain. while the clustering of these proteins with the tlr group is quite convincing, based on direct inspection of the blast results, it is extremely difficult, indeed impossible with these data, to discern more discrete relationships within this large family. the reasons for the anomalous hierarchical clustering results with these genes may relate to the large number of lrrs present in these proteins and the very slight differences in pairwise similarities across the group. at different levels of stringency small differences in blast scores may be amplified by the tribemcl algorithm to result in membership of different clusters that do not share the expected hierarchical relationship. attempts to resolve the phylogeny of all the genes in this group using the t-coffee multiple alignment program were no more enlightening, resulting in a starburst pattern where the roots of each subfamily are too close to each other to resolve. despite these limitations, the tribemcl analysis has revealed a group of elrr proteins that are clearly more related to the tlr proteins than to other lrr_only proteins. nomenclature the current nomenclature of elrr proteins is very confusing, with multiple synonyms for many genes, many of which do not give accurate information on relationships. for example, there is a large number of proteins designated lrrcx, where x is a number. these names were apparently derived from large-scale genome projects and do not represent a specific subfamily of related proteins. for that reason we have proposed the names elfn and for one novel subfamily. we also identify another discrete cluster of six "novel" proteins, although whether they represent a true subfamily is open to debate. in addition, some novel proteins that group into small subfamilies with lrrc21/pal, chad, lrrc and tpbg/5t could be given names to reflect that fact. finally, while lrrn and form a subfamily, the recently named nlrr is not in fact a member of this subfamily. it does not have the ig domain present in these genes and does not cluster with those genes at any parameters. comparative analyses of major groups for the purposes of some of the analyses we split the elrr proteins into four groups, based on architecture and clustering results. the lrr_ig/fn group includes the largest percentage of mammal-specific subfamilies, many with multiple members. almost all of the proteins in this group are associated with the membrane, either type i tm or gpi-linked. the majority of these subfamilies show discrete expression in the nervous system and many of them have been shown to have functions in neural development and/or have been implicated in neurological or psychiatric disease. expansion of this class of proteins is thus correlated with the evolution of the complex mammalian brain and plausibly contributed to it by providing the requisite specificity of cellular interactions to mediate a large number of selective connectivity decisions. we have identified a novel mammal-specific lrr_fn subfamily, the elfn proteins, with discrete nervous system expression. the lrr_only group shows independent diversification in flies and mammals, with a large number of singletons, suggesting rapid sequence divergence. this group also contains a number of proteins implicated in nervous system development or function including the nogo-receptor, lrrtm and slitrk families, and connectin, gp tpbg/5t and nyx for example. we have identified six proteins in another novel mammal-specific cluster, the elron cluster, several of which show highly suggestive expression patterns in the developing nervous system. we have also discovered a number of novel fly proteins in this class that are similarly discretely expressed in the embryonic nervous system. the lrr_tollkin group shows a different pattern of evolution, with parallel expansions in flies and mammals, of both the toll-like receptor genes and of the genes that cluster with them. in mammals, the latter include cpn gp and lrrc, which form a subcluster and which are also arrayed in tandem on chromosome in humans. these proteins have diverse binding partners and biochemical functions but are all involved in inflammation in some way: as a regulatory subunit of carboxypeptidase, as a component of the platelet glycoprotein complex, and as a mediator of the glial response to β-amyloid, respectively. they form a slightly larger cluster with igfals, the acid-labile subunit of insulin growth factor, which regulates igf signaling and with the novel gene kiaa which has an fn domain in addition to the lrr domain. lrrc32/garp and its paralogue lrrc also cluster with this group at e- level but not at some lower levels. the novel fly gene cg also clusters with this group at e- level and with other fly genes including chaoptin at some other levels. whether it can be said to be directly orthologous to any of these mammalian proteins is hard to determine. the other fly genes in this group are mostly novel and include cg40500-pd, which has an fn domain and which shows very discrete expression in the midline of the embryonic nervous system. the lrr_other group is an arbitrary default group as it contains many unrelated genes or subfamilies. nevertheless, it is interesting to note that this group contains the highest percentage of genes with orthologues across all species, including worms. this group also includes the mammalian lgi subfamily, recently implicated in epilepsy and myelination. human-mouse differences only a small number of proteins are specific to either human or mouse. there are two cases where there are genes in humans that are not represented in mouse that both seem to be caused by specific loss in mice, rather than representing human-specific genes. the human gene synleurin appears to have been pseudogenised in rodents, although it is present in many other species besides humans. also, mxra, a paralogue of the large secreted protein igsf is not detectable in the mouse genome. there is also a small number of examples where there has been independent expansion of subfamilies in either humans or mice. these include the toll-like receptors tlr in humans and tlr and in the mouse. they also include the unusual subfamily of lrrc genes, which is represented by a single gene in the mouse but multiple, highly related genes in humans. these are located in tandem on chromosome and have arisen from multiple duplications of the brca region in primates. the extracellular domains of these tm proteins are characterised by six predicted lrrs but these make up only a small fraction of the overall protein, which is highly variable in length. the functions and expression patterns of these unusual proteins are unknown.  this survey presents a comprehensive overview of the repertoire of elrr proteins in various species and their inter-relationships. as such, it provides the necessary foundation for a systematic analysis of the functions of this class of genes, which are likely to include prominently neural development, innate immunity and inflammation. in particular, expansion of the elrr proteome is correlated with increasing complexity of the nervous system. given the functions and discrete expression patterns of many known members, it seems likely that this superfamily, including the novel proteins identified here, could provide the requisite specificity of cellular interactions to mediate a large number of selective connectivity decisions. 
our analyses reveal several clusters of novel genes, including two elfn genes, encoding transmembrane proteins with elrrs and an fn domain, and six genes encoding transmembrane proteins with elrrs only. we have also identified a number of novel fly elrr proteins with discrete expression in the embryonic nervous system. we use convergent evidence from several transmembrane-prediction and motif-detection programs, including a customised algorithm, lrrscan, to identify elrr proteins, and a hierarchical clustering method based on tribemcl to establish their evolutionary relationships. this study provides the necessary foundation for a systematic analysis of the functions of this class of genes, which are likely to include prominently innate immunity, inflammation and neural development, especially the specification of neuronal connectivity. many of these are expressed in discrete patterns in the developing mouse brain, notably in the thalamus and cortex. we group elrr proteins into several classes: those with only lrrs, those that cluster with toll-like receptors, those with immunoglobulin or fibronectin-type domains and those with some other domain. leucine-rich repeats are highly versatile and evolvable protein-ligand interaction motifs found in a large number of proteins with diverse functions, including innate immunity and nervous system development.
 leucine-rich repeats are highly versatile and evolvable protein-ligand interaction motifs found in a large number of proteins with diverse functions, including innate immunity and nervous system development. here we catalogue all of the extracellular lrr proteins in worms, flies, mice and humans. we use convergent evidence from several transmembrane-prediction and motif-detection programs, including a customised algorithm, lrrscan, to identify elrr proteins, and a hierarchical clustering method based on tribemcl to establish their evolutionary relationships.  this yields a total of proteins, many of them of unknown function. we group elrr proteins into several classes: those with only lrrs, those that cluster with toll-like receptors, those with immunoglobulin or fibronectin-type domains and those with some other domain. these groups show differential patterns of expansion and diversification across species. our analyses reveal several clusters of novel genes, including two elfn genes, encoding transmembrane proteins with elrrs and an fn domain, and six genes encoding transmembrane proteins with elrrs only. many of these are expressed in discrete patterns in the developing mouse brain, notably in the thalamus and cortex. we have also identified a number of novel fly elrr proteins with discrete expression in the embryonic nervous system.  this study provides the necessary foundation for a systematic analysis of the functions of this class of genes, which are likely to include prominently innate immunity, inflammation and neural development, especially the specification of neuronal connectivity. 