Abstract

The extensive similarities between the genomes of human and model organisms are the foundation of much of modern biology, with model organism experimentation permitting valuable insights into biological function and the aetiology of human disease. In contrast, differences among genomes have received less attention. Yet these can be expected to govern the physiological and morphological distinctions apparent among species, especially if such differences are the result of evolutionary adaptation. A recent comparison of the draft sequences of mouse and human genomes has shed light on the selective forces that have predominated in their recent evolutionary histories. In particular, mouse-specific clusters of homologues associated with roles in reproduction, immunity and host defence appear to be under diversifying positive selective pressure, as indicated by high ratios of non-synonymous to synonymous substitution rates. These clusters are also frequently punctuated by homologous pseudogenes. They thus have experienced numerous gene death, as well as gene birth, events. These regions appear, therefore, to have borne the brunt of adaptive evolution that underlies physiological and behavioural innovation in mice. We predict that the availability of numerous animal genomes will give rise to a new field of genome zoology in which differences in animal physiology and ethology are illuminated by the study of genomic sequence variations.

INTRODUCTION

Chordate genome sequences, now arriving as a trickle, will soon be a flood. Draft sequences of the mouse ( 1 ), puffer fish ( 2 ), sea squirt ( 3 ) and human genomes ( 4 , 5 ) arrived in the last 2 years, and those of rat ( 6 ), zebra fish ( 7 ), chicken ( 8 ) and chimpanzee ( 9 ) are forthcoming. As sequenced vertebrate genomes accumulate, their comparison is revealing the genes for a core set of proteins that are essential for vertebrate function. This is expediting the study of basic and pathologic biology and allows us to discern our place on the ‘tree of life’.

The remaining genes—those that are not common to all vertebrates—represent the ‘genomic fingerprint’ that best characterizes each taxonomic group. Their analysis can reveal their rich individual histories: how they arose, how they have been shaped by natural selection, and, often, how they perished. A comparison of the two most closely related available vertebrate genomes, those of human and mouse, already illuminates the contrasting fortunes of different genes. For some of these, the molecular clock of change ticks slowly, with few changes over tens of millions of years. For others, the pace of change is such that substantial genetic differences are evident even between different individuals of the same species.

Here we shall be reviewing early findings from the comparison of the mouse and human genomes. These show that at the vanguard of evolutionary change are gene clusters whose functions lie at the heart of competitive struggles, either among individuals of the same species, as in mate selection, or between different organisms, as in disease and parasitism. These observations at the genomic scale provide insights into adaptive evolution that confirm conclusions drawn from many smaller-scale studies on individual genes, including those involved in placentation ( 10 ), immunity ( 1113 ), olfaction ( 14 ) and reproduction ( 15 , 16 ).

Comparative genetics is now supplanting morphology in the determination of animal taxonomy (for example 1720 ). In future projects, insights in natural history might soon be based as much on comparative genomics as on physiological and behavioural observations. We predict that, as more vertebrate genomes become available, genome analyses will lay the foundation for a new discipline, genome zoology, which we define to be the study of the physiology and evolution of animals using genomic information.

MOUSE AND HUMAN GENOME COMPARISONS

Mouse and human are eutherian mammals which diverged an estimated 65–110 million years (Myr) ago ( 1 , 21 , 22 ). Both species have similar numbers of protein coding genes (∼30 000) ( 1 ). So what genetic differences underlie their distinctive morphologies and physiologies? One possibility is that these differences derive from changes in non-coding ( 23 ) and/or coding sequences that would have left unaltered the gene counts in the human and mouse genomes. Another is that they have arisen from gene deletions ( 24 ) and/or duplications specific to either of the lineages.

As part of the initial analysis of the draft mouse genome ( 1 ), we examined first the set of genes for which there are single counterparts in each genome (i.e. single ‘orthologues’, genes that have arisen via speciation), before considering the set of genes that are absent in the human. We found that a small set of orthologues implicated in reproduction, host defence and immunity have been subject to positive selection ( 1 , 25 ), leading to extensive innovation in these physiological systems.

Before we discuss the connections between these rapidly evolving genes and rodent physiology, it is appropriate first to outline both the measurement of selection and the evolutionary mechanisms of gene birth and death.

EVOLUTIONARY SELECTION IN ORTHOLOGUES

To identify either rapidly evolving or highly conserved genes, it is necessary to have some measure of selective pressures. Sequence identity alone is too blunt a tool for closely related species. For example, although the majority (70.1%) of amino acids in mouse and human orthologues are conserved, much of this conservation is due to slow rates of neutral substitution in both lineages rather than natural selection ( 1 ). Determining whether a gene sequence is under either positive or negative selection requires a knowledge of the background mutation rate, and is best accomplished by the calculation of the non-synonymous to synonymous substitution ratio ( KA / KS , dN / dS or ω; see the Appendix) ( 2628 ).

Our latest study ( 1 ) used 12 845 mouse and human orthologue pairs to show that: (a) highly structured, domain-containing, regions are subject to greater purifying selection than are less-structured inter-domain regions; (b) enzymes are also subject to greater purifying selection than non-enzymes; and (c) domains that are secreted, as opposed to cytoplasmic or nuclear, are under reduced purifying selection. Table 1 shows the mouse and human orthologues that showed highest KA / KS values in our study.

LOCAL GENE DUPLICATION AND RAPID EVOLUTION

It has been estimated that 1% of mammalian genes experience a duplication event every Myr, and that, on average, silencing of one of the resultant gene pair under reduced selection requires ∼4 Myr ( 29 ). Each of the rodent and primate lineages therefore has had ample opportunity to augment their common gene repertoires with significant numbers of duplicated genes in the 65–110 Myr period since their divergence. However, such lineage-specific duplications represent less than 20% of all human or mouse genes ( 1 ).

As we shall discuss, gene duplication in mammals appears to occur non-uniformly with clear preferences for immunity, host defence, reproduction and KRAB transcription factor genes. Since orthologues in these categories are also evolving rapidly (high KA / KS values), positive selection has acted not only to diversify sequences but also to fix gene duplicates in the population.

Gene duplication events are known to generate clusters of neighbouring homologous genes ( 30 ), such as the HOX ( 31 ), major histocompatibility complex (MHC) ( 32 ) and globin ( 33 ) gene clusters. Several gene clusters appear to have arisen recently, for example MHC Class Ib genes (evolving independently in mouse and human) ( 11 ), the morpheus gene family in hominoids ( 34 ), and the Naip cluster in mice, which differs in gene number between two laboratory mouse strains ( 35 ).

Gene birth

Unequal cross-over (recombination between non-allelic genes) is responsible for local gene duplication and rearrangement of gene clusters. The most striking examples of rapid gene family population changes are the olfactory receptor (OR) gene family. Extensive and recent gene duplications are manifest for mammalian ( 14 , 36 ) and insect ( 37 ) ORs. The plasticity of this family is thought to reflect the importance of olfaction in animal feeding and mating habits.

Intra-locus gene conversion (the nonreciprocal transfer of genetic information from one DNA molecule to a neighbouring homologous counterpart) is thought to drive sequence diversification. Both of these phenomena are more likely to occur for clusters containing many homologues that are highly sequence-similar. Evidence exists for the diversification by gene conversion and unequal cross-over of some of the gene families highlighted here or in the mouse genome paper ( 1 ) such as ORs ( 38 , 39 ), cytochrome P450s ( 40 , 41 ), and the MHC ( 42 ).

Gene death

Many mouse-specific gene clusters contain inactive gene copies or pseudogene sequences ( 1 ). Again the most striking example of this is the OR family. Mouse OR proteins are G protein-coupled receptors that are expressed in the olfactory epithelium from which neural signals are propagated to the olfactory bulb in the brain ( 14 , 43 ). Twenty percent of mouse ORs are pseudogenes and this proportion is even higher (∼60–70%) in humans ( 14 , 36 , 44 , 45 ). The high proportion of human OR pseudogenes has been linked to a proposed poorer sense of smell in humans ( 36 ). Consistent with this hypothesis only pseudogene counterparts have been found in the human and bovine genomes for the mouse Trp2 ion channel, which is thought to have a downstream role in the transduction of pheromone binding in the olfactory vomeronasal organ (VNO) ( 46 ).

The evolution of these gene clusters fits into a ‘birth-and-death’ gene model ( 36 , 47 ) where the population of active genes is increased by gene duplication and decreased by pseudogene creation. Although all pseudogenes are, by definition, functionally inactive, some still may not be selectively neutral. Their preservation can contribute to the incidence of unequal cross-over and, hence, the birth of new genes via local duplication. Unprocessed pseudogenes represent degenerated genes which arise due to loss of selection constraints ( 1 ). Examples of the loss of function of erstwhile beneficial genes are interesting in that they signal major changes in the underlying selection constraints. For example, although the mouse has an extensive family of Ly 49 natural killer T cell receptors, the proto- Ly 49 gene in the last common ancestor of mouse and human appears only to be preserved in the human lineage as a solitary pseudogene ( 48 ).

MOUSE-SPECIFIC GENE CLUSTERS

Mouse-specific gene clusters represent an evolutionary record of recent innovation in the rodent gene repertoire. Members of the clusters are under relaxed purifying selection as measured by KA / KS ratios, and are undergoing rapid sequence evolution as genes accrue new functions subsequent to local gene duplication ( 1 ). Genes involved in olfaction, reproduction, immunity and host defence, gustation and transcriptional regulation are significantly over-represented among such clusters.

Olfaction

A recurring theme among these genes is the centrality of olfaction in mouse-specific physiology and behaviour ( 49 ). The rapid duplication and inactivation of OR genes has already been commented upon. In mouse, OR genes occur in either 46 or 47 clusters within the genome ( 1 , 36 ) and have proliferated on the basis of local gene duplication. The rapid evolution of at least some of these genes may be attributable to their prominent roles in rodent reproduction. For example, the V1r subset of OR genes, which is thought to mediate pheromone-induced social and sexual behaviour ( 50 ), has been subject to significantly reduced purifying selection, with typical KA / KS values of 0.5 ( 51 ).

Reproduction

For the remaining (non-OR) gene clusters in the mouse genome, our studies identified 25 gene clusters which had no counterpart in the human. Fourteen of these relate to sexual reproduction, with roles influencing hormone metabolism, sexual behaviour or placentation (Fig.  1 ).

One of these clusters contains MHC class 1b genes. These genes have evolved independently in both mouse and humans ( 12 ). MHC associated pheromone cues influence mate selection, oestrus synchronization ( 52 , 53 ), oestrus suppression ( 54 , 55 ), male-induced termination of pregnancy ( 56 ) and puberty acceleration ( 57 , 58 ). The MHC 1b gene cluster may represent a good candidate for a yet-to-be-determined molecule linking MHC to odour and reproductive behaviour ( 59 ).

Another of these clusters contains androgen binding protein alpha (ABPα) homologues. ABPα is a saliva-based pheromone which influences mouse mating behaviour. Its sub-species specific signature is a proposed cue used by female mice to select mates with similar ABPα genotype in order to avoid inter-subspecies breeding ( 60 , 61 ). A mouse gene cluster of homologues of a vomeronasally detected hamster pheromone (aphrodisin) that influences male mouse mating behaviour ( 62 ) was also apparent.

Two mechanisms appear to act antagonistically in promoting genetic heterogeneity or homogeneity. On one hand, sexual selection of MHC variation maintains genetic diversity especially in isolated populations and prevents ‘inbreeding depression’. On the other, ABPα may help to preserve local adaptations specialized for specific environmental niches being lost in ‘outbreeding depression’ ( 63 ).

Of all mammalian organs, the placenta has been considered as the most variable in structure ( 6466 ). Placental innovation is reflected in the considerable difference in the placental gene repertoires for human and mouse. For example, on mouse chromosome 13 is a large cluster of prolactin-related genes for which humans only possess a single counterpart (Fig.  2 ) ( 67 ). Different roles for the mouse prolactin-like genes in formation and maintenance of the placenta are known ( 6870 ) including development of new and existing blood vessels, maintenance of blood content homeostasis and manipulation of the immune system. Recently, the reproduction-stimulated secretion of prolactin has been associated with neurogenesis in the olfactory system of the adult mouse brain, suggesting a novel feed-back loop affecting the sensory system ( 71 ).

Host defence and immunity

In addition to the MHC class Ib genes already discussed, four other mouse-specific gene clusters were identified that have immunity and host defence functions ( 1 ): two clusters of β-defensin antimicrobial genes, WAP domain antimicrobial genes and type A ribonucleases (RNase As). Like MHC genes, many of these clusters with host defence and immunity functions may also play a part in reproduction. For example, modulation of MHC gene expression in the placenta has a prominent role in ensuring that the fetus is not rejected in utero ( 72 ). In addition, WAP ( 73 , 74 ) and prolactin-inducible protein homologues ( 75 ) have functions in both immunity and reproduction.

RNase As are notable as the only enzymatic representative of families that are absent in non-chordate genomes ( 4 ). This lineage-specificity is probably due to their enhanced rate of gene duplication and positive selection ( 76 , 77 ) which, in turn, is probably a consequence of their antiviral functions ( 78 ).

Perhaps the most striking difference between the repertoires of immunity genes in mouse and human concerns inhibitory and activating natural killer (NK) receptor genes ( 79 ). In mouse, a large family of Ly 49 genes appears to perform roles that are undertaken by KIR genes in human. Although these families are non-homologous, they both recognize MHC class I epitopes, are expressed clonally on NK cells, are polymorphic, and transduce similar downstream signalling pathways ( 79 ). As such, they represent an excellent example of functional convergence that exploits non-homologous genes ( 80 ), in contrast with the parallel evolution of homologous MHC class Ib genes in mouse and human.

Ingested and inhaled substances, including pheromones, are likely to be degraded by members of the cytochrome P450 family ( 81 ). These enzymes catalyse a wide range of reactions on different substrates. From their huge diversity in number among different species ( 1 , 82 ), their propensity to be found as pseudogenes, their localization to clusters in genomes, and their high KA / KS values ( 1 , 82 ), it can be inferred that this family and its pluripotent functions lie at the forefront of adaptive change in animals.

The impact of selection on immunity genes has been considerable. Twenty-seven mouse and human genes that have high KA / KS values perform immunity or host defence roles (Table 1 ). By contrast, only seven of these 50 rapidly evolving genes have roles in reproduction. Since only single orthologues are represented in Table 1 , it would appear that, in general, immunity genes and reproduction genes have suffered contrasting consequences of strong selection pressures. Immunity genes evolve most rapidly, whereas reproduction genes duplicate with less rapid sequence divergence.

Gustation

Three of the most rapidly-evolving pairs of mouse and human orthologues (STG, TAS2R10 and TAS2R16; Table 1 ) are found in taste-buds and are associated with gustation ( 8386 ). TAS2R16 and TAS2R10 are two G protein-coupled receptors, distantly related to ORs, that are associated with transduction of bitter taste ( 8386 ). The greatest sequence variability is to be found in their extracellular loops, perhaps reflecting adaptations allowing detection of large numbers of flavours ( 83 ), and low concentrations of bitter, often harmful or poisonous, substances associated with the phenomenon of conditioned taste aversion ( 8789 ).

Transcription regulation

A fourth arena that appears to have responded to the forces of adaptive evolution is regulation of transcription. Of all the domain families examined in the initial analysis of the mouse genome ( 1 ), the Krüppel -associated box (KRAB) domain family appears to be undergoing the most rapid evolution. This domain family has the highest median KA / KS value (0.28) of all domains ( 1 ) and, as a likely result of high sequence divergence, cannot be detected outside of tetrapods ( 4 , 5 , 90 ).

KRAB zinc finger (KRAB-ZnF) genes are known to function as DNA binding-dependent transcriptional repressors ( 91 ) and are found in clusters in the human genome ( 9294 ) suggesting gene duplication as a means of divergence. These genes mostly encode proteins that contain an N-terminal KRAB domain followed by multiple DNA-binding C2H2 zinc fingers ( 95 , 96 ). The origins of the selectionary pressures shaping their evolution remain unknown, however, since the functions of KRAB-ZnF are as yet unclear. What is known is that different sub-families of KRAB-ZnF proteins exhibit restricted expression patterns ( 93 ), and some have been associated with the commitment of myeloid and lymphoid cells to a specific lineage by gene silencing in non-lineage cells ( 9799 ). Thus KRAB-ZnF genes may control the phenotype of cells descendent from a common pluripotent cell, as in the lymphoid and myeloid systems. The diversification of KRAB domain genes and the radiation of adaptive immune response genes may therefore be intimately related.

THE IMPACT OF CONFLICT IN THE MOUSE AND HUMAN GENOMES

When comparing the mouse genome with that of human, the overall impression is one of similarity. Eighty percent of genes have one-to-one corresponding counterparts in the other's genome and, in genomic alignments, 40% of nucleotides are identical. It is, therefore, all the more striking then that lineage-specific genes often exhibit four features simultaneously: (i) they are clustered in compact genomic regions; (ii) they are accompanied in these clusters by homologous pseudogenes; (iii) they show greater non-synonymous nucleotide substitution rates than most other genes; and (iv) they preferentially perform roles in life-or-death conflicts.

Such genes are evolving, via duplication and sequence divergence, at rapid rates as a result of conspecific (reproduction) and interspecific (immunity) competition ( 100 ). For the latter, adaptation in one genome changes the degree of selection pressure on another, by inter alia modifying mating preferences, fertilization efficiencies, or survival rates of both parasites and their hosts ( 101103 ). The higher KA / KS values of secreted protein regions, relative to those of intracellular proteins ( 1 ), attest to this ‘genetic arms race’.

Mate selection, maternal behaviour and sexual aggression have genetic components in rodents that appear to be evolving particularly rapidly. Reproduction proteins are well documented as being subject to rapid change and play pivotal roles in speciation theories ( 104 ). It is no accident that the phylogenetic classifications Mammalia, Eutheria (placental mammals) and marsupials (from the Greek marsupion , meaning ‘pouch’) distinguish on the basis of reproductive anatomical features that have been subject to recent rapid adaptive change.

CONCLUSION

In this review we have highlighted the latest developments in comparative genomics. In mammalian evolution, positive selection has driven the adaptation of genomes through the interplay of gene birth, gene death and sequence change. Novel functions have emerged from alterations in both gene repertoire and amino acid sequence. These phenomena have been observed in the few, yet diverse, animal phyla for which we have sufficient sequence information. As genome sequences from a wider taxonomic range become available, however, it will be fascinating to explore whether the genetic targets of adaptive evolution are consistent throughout the animal world.

Genome comparisons will continue to highlight the core sets of proteins which are common to their shared ancestor. However, genomic differences are archives of the lineage-specific adaptations that have evolved since the last common ancestor in response to critical challenges. The study of these ‘genomic-fingerprints’, in the context of experimental data, heralds a new approach of genome zoology, which links animal physiological and behavioural adaptations to the underlying genomic differences.

ACKNOWLEDGEMENTS

We would like to thank, for their kind assistance and insightful discussions, the Mouse Genome Sequencing Consortium, and, in particular, Ewan Birney, Peer Bork, Richard Copley, Nick Dickens, Michele Clamp, David Haussler, Jim Kent, Eric Lander, Kerstin Lindblad-Toh and Bob Waterston. C.P.P. thanks Harry Charlton for interesting discussions on prolactin.

APPENDIX

KA / KS is the ratio of non-synonymous (amino-acid changing) to synonymous (silent) substitution rates in protein-coding genes. To calculate the KA / KS ratio, the codons of two protein-coding DNA sequences are aligned according to the amino acid pairwise alignment. K A is then estimated from the numbers of non-synonymous (amino acid replacing) substitutions at each non-synonymous site. Similarly, KS is estimated from the numbers of synonymous substitutions per synonymous site. If

  • KAKS , little or no selection has occurred among different sites;

  • KAKS , strong purifying selection has reduced the fixation rate of deleterious mutations; and

  • KA > KS , the amino acid change fixation rate is higher than the neutral substitution rate. This is direct evidence for positive selection of amino acid substitutions which offer a selective advantage to the organism.

The KA / KS ratio has generally been calculated as an average over all codons in a gene and over the entire evolutionary time since their divergence. However, molecular adaptation of a protein under positive selection often occurs only for a few residues and perhaps only in certain lineages. A canonical case of this would be an enzyme where residues involved in substrate selection might be under positive selection while the rest of the protein, in particular the catalytic site residues, might still be strongly conserved. In these cases, the KA / KS averaged over the whole protein might still be much smaller than 1. In most cases, therefore, positive selection can only be proved by calculating site-specific KA / KS ratios. This requires orthologous sequences from multiple organisms selected from across the phylogenetic tree. A genome-wide survey of positively selected proteins in vertebrates, therefore, still awaits the further availability of orthologues from more diverse genomes such as chicken and zebrafish.

*

To whom correspondence should be addressed. Tel: +44 1865272175; Fax: +44 1865272420; Email: chris.ponting@anat.ox.ac.uk

Figure 1. A schematic representation of the roles of gene clusters known to be involved in pheromonal and hormonal responses in the mouse. Red text signifies mouse-specific gene clusters as defined and discussed here or in Waterston et al. ( 1 ). Genes whose names are in black have been moderately expanded within clusters in the mouse genome. Blue lines represent neuronal connections between the highlighted organs. Red arrows represent the proposed pathway of interactions between genes, although intervening steps are not always shown. Green arrows represent the proposed pathway by which the metabolism of hormones may influence physiology. It is not assumed that all of these genes will influence physiology or behaviour at any one time point. The diagram represents a gender-non-specific mouse. Mouse-specific gene clusters appear to be involved in hormone metabolism. The cluster of 3-beta-hydroxysteroid dehydrogenases (3-HSD) on chromosome 3 is likely to regulate glucocorticoid, oestrogen and androgen concentrations by controlling the binding of hormone to steroid hormone receptors ( 105107 ). The cytochrome P450 cluster located on mouse chromosome 4 represents an expansion within the CYP4A family. In the rat the CYP4A family is expressed in the liver and kidney ( 108 , 109 ) and in males is hormonally regulated by testosterone ( 110 , 111 ). Members of the seminal vesicle secretory protein (SVS) cluster on mouse chromosome 2 are rapidly evolving, androgen-regulated proteins involved in the formation of the copulatory plug and influence the survival and efficacy of spermatozoa ( 112114 ). Multiple lipocalin-like gene clusters were also identified. The proteins are proposed to bind odorant molecules in the mucous layer overlying the receptors of the mouse vomeronasal organ ( 115 , 116 ). Prolactin has varying roles in pregnancy and placentation (Fig.  2 ). The release of prolactin has been linked to neurogenesis in the region of the olfactory system of mice ( 71 ); this is represented as a green dashed line.

Figure 1. A schematic representation of the roles of gene clusters known to be involved in pheromonal and hormonal responses in the mouse. Red text signifies mouse-specific gene clusters as defined and discussed here or in Waterston et al. ( 1 ). Genes whose names are in black have been moderately expanded within clusters in the mouse genome. Blue lines represent neuronal connections between the highlighted organs. Red arrows represent the proposed pathway of interactions between genes, although intervening steps are not always shown. Green arrows represent the proposed pathway by which the metabolism of hormones may influence physiology. It is not assumed that all of these genes will influence physiology or behaviour at any one time point. The diagram represents a gender-non-specific mouse. Mouse-specific gene clusters appear to be involved in hormone metabolism. The cluster of 3-beta-hydroxysteroid dehydrogenases (3-HSD) on chromosome 3 is likely to regulate glucocorticoid, oestrogen and androgen concentrations by controlling the binding of hormone to steroid hormone receptors ( 105107 ). The cytochrome P450 cluster located on mouse chromosome 4 represents an expansion within the CYP4A family. In the rat the CYP4A family is expressed in the liver and kidney ( 108 , 109 ) and in males is hormonally regulated by testosterone ( 110 , 111 ). Members of the seminal vesicle secretory protein (SVS) cluster on mouse chromosome 2 are rapidly evolving, androgen-regulated proteins involved in the formation of the copulatory plug and influence the survival and efficacy of spermatozoa ( 112114 ). Multiple lipocalin-like gene clusters were also identified. The proteins are proposed to bind odorant molecules in the mucous layer overlying the receptors of the mouse vomeronasal organ ( 115 , 116 ). Prolactin has varying roles in pregnancy and placentation (Fig.  2 ). The release of prolactin has been linked to neurogenesis in the region of the olfactory system of mice ( 71 ); this is represented as a green dashed line.

Figure 2. A schematic representation of the expansion of prolactin homologous genes located in a gene cluster on mouse chromosome 13. Mouse RU2, Hdgfrp, prolactin, Sox4 and FLJ20342 genes (represented by red blocks) are syntenic with human chromosome 6. Mouse prolactin-like genes are numbered 1–22, where 1=Prl (X02892), 2=Csh1 (XM_127243.2), 3=PL-Iα (M35662), 4=PL-Iβ (XM_127244.1), 5=PLP-J (AF525154), 6=PL-II (M14647), 7=PLP-I (NP_080172), 8=PLP-B (AF015563), 9=DPRP (AF015729), 10=PLP-K (NP_079808.1), 11=PLP-Cα (AF090140), 12=PLP-Cg (AF466150), 13=PLP-Cβ (AF158744), 14=PLP-Cδ (AF525158), 15=PLP-N (AF525156), 16=PLP-E (AF020525), 17=PLP-F (AF020524), 18=prolactin related protein precursor (NP_035250.1), 19=Prf (NM_031191.1), 20=PLP-A (AF015562), 21=XM_110051 and 22=PLP-L (AF226611). Positions of these genes are based on the Mouse February 2002 and Human November 2002 data freezes available at UCSC genome browser ( http://genome.cse.ucsc.edu/ ). Orientations of transcription are represented by arrowheads. In the mouse the act of mating stimulates prolactin production by the pituitary gland, initiating a continued and highly regulated hormonal cascade, which includes members of the family of prolactin-like proteins ( 117 ). So strong is this induction that even mating with an infertile male will induce prolactin release and a pseudopregnancy ( 117 ). The varying roles of the prolactin-like genes have been reported previously ( 6870 ). Prolactin homologues act in concert to influence the development of new and existing blood vessels, manipulate the immune system and maintain blood content homeostasis. Specifically proliferin, also known as mitogen-regulated protein, has been shown to affect angiogenesis by capillary endothelial cell migration, whilst proliferin-related protein acts antagonistically ( 118 ). PLP-A has been reported to bind to and influence the cytotoxicity of maternal natural killer cells ( 119 ), and PLP-E shown to stimulate the differentiation of megakaryocytes to maintain the platelet content of maternal blood ( 69 ).

Figure 2. A schematic representation of the expansion of prolactin homologous genes located in a gene cluster on mouse chromosome 13. Mouse RU2, Hdgfrp, prolactin, Sox4 and FLJ20342 genes (represented by red blocks) are syntenic with human chromosome 6. Mouse prolactin-like genes are numbered 1–22, where 1=Prl (X02892), 2=Csh1 (XM_127243.2), 3=PL-Iα (M35662), 4=PL-Iβ (XM_127244.1), 5=PLP-J (AF525154), 6=PL-II (M14647), 7=PLP-I (NP_080172), 8=PLP-B (AF015563), 9=DPRP (AF015729), 10=PLP-K (NP_079808.1), 11=PLP-Cα (AF090140), 12=PLP-Cg (AF466150), 13=PLP-Cβ (AF158744), 14=PLP-Cδ (AF525158), 15=PLP-N (AF525156), 16=PLP-E (AF020525), 17=PLP-F (AF020524), 18=prolactin related protein precursor (NP_035250.1), 19=Prf (NM_031191.1), 20=PLP-A (AF015562), 21=XM_110051 and 22=PLP-L (AF226611). Positions of these genes are based on the Mouse February 2002 and Human November 2002 data freezes available at UCSC genome browser ( http://genome.cse.ucsc.edu/ ). Orientations of transcription are represented by arrowheads. In the mouse the act of mating stimulates prolactin production by the pituitary gland, initiating a continued and highly regulated hormonal cascade, which includes members of the family of prolactin-like proteins ( 117 ). So strong is this induction that even mating with an infertile male will induce prolactin release and a pseudopregnancy ( 117 ). The varying roles of the prolactin-like genes have been reported previously ( 6870 ). Prolactin homologues act in concert to influence the development of new and existing blood vessels, manipulate the immune system and maintain blood content homeostasis. Specifically proliferin, also known as mitogen-regulated protein, has been shown to affect angiogenesis by capillary endothelial cell migration, whilst proliferin-related protein acts antagonistically ( 118 ). PLP-A has been reported to bind to and influence the cytotoxicity of maternal natural killer cells ( 119 ), and PLP-E shown to stimulate the differentiation of megakaryocytes to maintain the platelet content of maternal blood ( 69 ).

Table 1.

The fifty mouse and human 1 : 1 orthologues with highest KA / KS values

KA / KS RefSeq code Approved symbol Description  Category a 
1.05 NP_006410 CXCL13 Small inducible cytokine B13 precursor (CXCL13) 
0.93 NP_149024 TSLP Thymic stromal lymphopoietin 
0.84 NP_003317 TNFSF4 OX40 ligand (OX40L) (Glycoprotein GP34) 
0.83 NP_054789 STG Taste-bud specific gene; STG protein 
0.78 NP_058647 CKLF1 Chemokine-like factor 2 
0.77 NP_001992 FCER1A High affinity immunoglobulin epsilon receptor alpha-subunit precursor 
0.76 NP_002997 SELPLG P-selectin glycoprotein ligand 1 precursor (PSGL-1) 
0.75 NP_057351 TONDU Transcriptional coactivator 
0.75 NP_000610 IFNG Interferon gamma precursor (IFN-gamma) 
0.75 NP_058641 TAS2R16 Bitter taste receptor T2R16 
0.74 NP_000130 MS4A2 High affinity immunoglobulin epsilon receptor beta-subunit (FcERI) 
0.74 NP_006408 IFI44 Interferon-induced, hepatitis C-associated microtubular aggregate protein 
0.74 NP_006655 CCL27 Small inducible cytokine A27 precursor 
0.73 NP_061113 TREM1 Triggering receptor expressed on monocytes 1 
0.73 NP_005182 CD80 T lymphocyte activation antigen CD80 precursor 
0.72 NP_005407 SPRR3 Small proline-rich protein 3 
0.72 XP_114180 C20orf70 Parotid secretory protein; host defense protein 
0.71 NP_085144 APOL6 Apolipoprotein L, 6 
0.71 XP_009097 PPP1R15A Protein phosphatase 1, regulatory (inhibitor) subunit 15A 
0.71 NP_077299 MGC11271 Pre-T/NK cell associated protein 
0.71 XP_059814 ctm-1 Testis-specific protein 
0.70 NP_009110 INSL6 Insulin-like 6 
0.70 NP_000591 IL6 Interleukin 6 (interferon, beta 2) 
0.70 NP_002402 SCGB2A2 Secretoglobin, family 2A, member 2 
0.70 NP_076410 TAS2R10 Taste receptor, type 2, member 10 
0.70 NP_055292 C20orf10 Chromosome 20 open reading frame 10 
0.70 NP_443164 PORIMIN Pro-oncosis receptor inducing membrane injury gene 
0.69 NP_067024 MS4A7 High-affinity immunoglobulin epsilon receptor beta subunit 
0.69 NP_001773 CD72 CD72 antigen 
0.69 NP_006502 RSC1A1 Sodium-D-glucose cotransporter 
0.68 NP_005502 MLANA Melan-A; melanoma antigen recognized by T-cells 1 
0.68 NP_112579 GSG1 Germ cell associated 1 
0.67 NP_071342 CXCL16 Chemokine (C-X-C motif) ligand 16 
0.66 NP_079494 ULBP1 UL16 binding protein 1; MHC class I-related protein 
0.66 NP_002695 PPBP Pro-platelet basic protein (chemokine (C-X-C motif) ligand 7) 
0.66 NP_006841 IL24 Interleukin 24 
0.66 NP_054883 FXYD5 Dysadherin; FXYD domain containing ion transport regulator 5 
0.66 NP_001857 COX7B Cytochrome c oxidase subunit VIIb precursor 
0.65 NP_112193 DEFB126 Epididymal secretory protein ESP13.2; defensin, beta 126 I R 
0.65 NP_060186 MS4A12 Membrane-spanning 4-domains, subfamily A, member 12 
0.64 NP_003708 NPFF Neuropeptide FF-amide peptide; regulates heart rate and blood pressure 
0.64 NP_009225 BRCA1 Breast cancer type 1 susceptibility protein 
0.63 NP_005471 TROAP Trophinin associated protein; role in embryo implantation 
0.63 NP_114113 USP26 Ubiquitin specific protease 26; expressed in spermatogonia 
0.63 NP_543021 DEFB129 Defensin, beta 129 
0.63 NP_112570 MRPS15 Mitochondrial ribosomal protein S15 
0.62 NP_005483 CST8 Cystatin 8 (cystatin-related epididymal specific) I R 
0.62 XP_130612 Ryf3 In rat, specifically expressed in olfactory mucosa 
0.62 NP_054739 LR8 LR8 lung fibroblast protein 
0.62 NP_002167 IFNB1 Interferon, beta 1, fibroblast 
KA / KS RefSeq code Approved symbol Description  Category a 
1.05 NP_006410 CXCL13 Small inducible cytokine B13 precursor (CXCL13) 
0.93 NP_149024 TSLP Thymic stromal lymphopoietin 
0.84 NP_003317 TNFSF4 OX40 ligand (OX40L) (Glycoprotein GP34) 
0.83 NP_054789 STG Taste-bud specific gene; STG protein 
0.78 NP_058647 CKLF1 Chemokine-like factor 2 
0.77 NP_001992 FCER1A High affinity immunoglobulin epsilon receptor alpha-subunit precursor 
0.76 NP_002997 SELPLG P-selectin glycoprotein ligand 1 precursor (PSGL-1) 
0.75 NP_057351 TONDU Transcriptional coactivator 
0.75 NP_000610 IFNG Interferon gamma precursor (IFN-gamma) 
0.75 NP_058641 TAS2R16 Bitter taste receptor T2R16 
0.74 NP_000130 MS4A2 High affinity immunoglobulin epsilon receptor beta-subunit (FcERI) 
0.74 NP_006408 IFI44 Interferon-induced, hepatitis C-associated microtubular aggregate protein 
0.74 NP_006655 CCL27 Small inducible cytokine A27 precursor 
0.73 NP_061113 TREM1 Triggering receptor expressed on monocytes 1 
0.73 NP_005182 CD80 T lymphocyte activation antigen CD80 precursor 
0.72 NP_005407 SPRR3 Small proline-rich protein 3 
0.72 XP_114180 C20orf70 Parotid secretory protein; host defense protein 
0.71 NP_085144 APOL6 Apolipoprotein L, 6 
0.71 XP_009097 PPP1R15A Protein phosphatase 1, regulatory (inhibitor) subunit 15A 
0.71 NP_077299 MGC11271 Pre-T/NK cell associated protein 
0.71 XP_059814 ctm-1 Testis-specific protein 
0.70 NP_009110 INSL6 Insulin-like 6 
0.70 NP_000591 IL6 Interleukin 6 (interferon, beta 2) 
0.70 NP_002402 SCGB2A2 Secretoglobin, family 2A, member 2 
0.70 NP_076410 TAS2R10 Taste receptor, type 2, member 10 
0.70 NP_055292 C20orf10 Chromosome 20 open reading frame 10 
0.70 NP_443164 PORIMIN Pro-oncosis receptor inducing membrane injury gene 
0.69 NP_067024 MS4A7 High-affinity immunoglobulin epsilon receptor beta subunit 
0.69 NP_001773 CD72 CD72 antigen 
0.69 NP_006502 RSC1A1 Sodium-D-glucose cotransporter 
0.68 NP_005502 MLANA Melan-A; melanoma antigen recognized by T-cells 1 
0.68 NP_112579 GSG1 Germ cell associated 1 
0.67 NP_071342 CXCL16 Chemokine (C-X-C motif) ligand 16 
0.66 NP_079494 ULBP1 UL16 binding protein 1; MHC class I-related protein 
0.66 NP_002695 PPBP Pro-platelet basic protein (chemokine (C-X-C motif) ligand 7) 
0.66 NP_006841 IL24 Interleukin 24 
0.66 NP_054883 FXYD5 Dysadherin; FXYD domain containing ion transport regulator 5 
0.66 NP_001857 COX7B Cytochrome c oxidase subunit VIIb precursor 
0.65 NP_112193 DEFB126 Epididymal secretory protein ESP13.2; defensin, beta 126 I R 
0.65 NP_060186 MS4A12 Membrane-spanning 4-domains, subfamily A, member 12 
0.64 NP_003708 NPFF Neuropeptide FF-amide peptide; regulates heart rate and blood pressure 
0.64 NP_009225 BRCA1 Breast cancer type 1 susceptibility protein 
0.63 NP_005471 TROAP Trophinin associated protein; role in embryo implantation 
0.63 NP_114113 USP26 Ubiquitin specific protease 26; expressed in spermatogonia 
0.63 NP_543021 DEFB129 Defensin, beta 129 
0.63 NP_112570 MRPS15 Mitochondrial ribosomal protein S15 
0.62 NP_005483 CST8 Cystatin 8 (cystatin-related epididymal specific) I R 
0.62 XP_130612 Ryf3 In rat, specifically expressed in olfactory mucosa 
0.62 NP_054739 LR8 LR8 lung fibroblast protein 
0.62 NP_002167 IFNB1 Interferon, beta 1, fibroblast 

a Functional classification: G, gustation; I, immunity and host defence; R, reproduction; T, transcription regulation; and O, other.

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