Vol 440 | 16 March 2006 | doi : 10.1038 / nature04562 LETTERS Multiple rounds of speciation associated with reciprocal gene loss in polyploid yeasts Devin R. Scannell1 * , Kevin P. Byrne1 * , Jonathan L. Gordon1 , Simon Wong1 & Kenneth H. Wolfe1 A whole-genome duplication occurred in a shared ancestor of the yeast species Saccharomyces cerevisiae , Saccharomyces castellii and Candida glabrata . Here we trace the subsequent losses of duplicated genes , and show that the pattern of loss differs among the three species at 20 % of all loci . For example , several transcription factor genes , including STE12 , TEC1 , TUP1 and MCM1 , are singlecopy in S. cerevisiae but are retained in duplicate in S. castellii and C. glabrata . At many loci , different species have lost different members of a duplicated gene pair , so that 4 7 % of single-copy genes compared between any two species are not orthologues . This pattern of gene loss provides strong evidence for speciation through a version of the Bateson Dobzhansky Muller mechanism , in which the loss of alternative copies of duplicated genes leads to reproductive isolation1 ,2 . We show that the lineages leading to the three species diverged shortly after the wholegenome duplication , during a period of precipitous gene loss . The set of loci at which single-copy paralogues are retained is biased towards genes involved in ribosome biogenesis and genes that evolve slowly , consistent with the hypothesis that reciprocal gene loss is more likely to occur between duplicated genes that are functionally indistinguishable . We propose a simple , unified model in which a single mechanism -- passive gene loss -- enabled whole-genome duplication and led to the rapid emergence of new yeast species . We used the Yeast Gene Order Browser -LRB- YGOB , ref 3 -RRB- to compare six yeast species , three of which diverged after their common ancestor experienced a whole-genome duplication -LRB- WGD -RRB- , and three of which diverged from this lineage before the WGD . The YGOB compares pairs of genomic regions from post-WGD species -LRB- S. cerevisiae4 ,5 , S. castellii6 and C. glabrata7 -RRB- to single genomic regions in pre-WGD species -LRB- Kluyveromyces waltii8 , Kluyveromyces lactis7 and Ashbya gossypii9 -RRB- -LRB- Fig. 1 -RRB- . We use the term ` ancestral locus ' to describe a locus in a pre-WGD species , or the corresponding duplicated pair of loci in a post-WGD species -LRB- that is , a column in Fig. 1 -RRB- . Synteny conservation enabled us to determine unambiguously whether each of 2,723 ancestral loci was retained in one or two copies in each post-WGD genome . If only one copy remained , the syntenic context allowed orthologues to be distinguished from paralogues -LRB- Fig. 1 -RRB- . The fate of an ancestral locus among the three post-WGD species can be classified into one of 14 possible patterns -LRB- Fig. 2 -RRB- . The most common pattern -LRB- Class 4 , seen at 1,957 ancestral loci or 72 % of the total -RRB- is that all three species have lost the same -LRB- orthologous -RRB- copy of the gene , such as in the LYS2 column in Fig. 1 . For clarity , we show this as three separate losses in Fig. 2 , but a loss could have occurred in the ancestor of two or three of the species . Of the ancestral loci , 210 -LRB- 8 % -RRB- remain duplicated in all three post-WGD species -LRB- Class 0 -RRB- . The other 556 ancestral loci -LRB- 20 % -RRB- have had variable fates among the three post-WGD species , which indicates that the consequences of WGD were still being sorted out when these lineages diverged . A striking example is the set of 18 genes that are single-copy in S. cerevisiae but double-copy in both S. castellii and C. glabrata -LRB- Class 1B -RRB- . Transcription factors are disproportionately overrepresented in this group -LRB- it includes STE12 , TUP1 , GAL11 , GCR2 , Figure 1 | Gene order relationships in the region around S. cerevisiae SSN6 and its homologues . Relationships based on YGOB output -LRB- http : / / wolfe.gen.tcd.ie / ygob -RRB- . Coloured boxes represent genes and are not drawn to scale . Chromosomal regions from each pre-WGD species are represented by one horizontal track each . The two corresponding regions in each post-WGD species are represented by two tracks -LRB- A and B -RRB- at the top and bottom . Chromosome , contig or scaffold numbers are indicated on the right . Homologous genes are arranged in columns . Thick grey horizontal bars connect genes that are immediate neighbours in the genome . Codes below columns indicate the gene loss class for that ancestral locus , as used in Fig. 2 . Columns without codes did not meet the criteria for scoring . 1 Smurfit Institute of Genetics , University of Dublin , Trinity College , Dublin 2 , Ireland . * These authors contributed equally to this work . 2006 Nature Publishing Group 341 LETTERS NATURE | Vol 440 | 16 March 2006 SFP1 , YAP3 and TYE7 ; P 0.001 by Fisher 's test -RRB- , which suggests that the transcriptional regulatory network in S. cerevisiae is simpler than in the other yeasts -LRB- Supplementary Table S1 -RRB- . MCM1 and TEC1 are also in a 1:2:2 relationship among the post-WGD genomes , but these two loci were not counted in Fig. 2 because the syntenic context around them is not completely conserved . S. cerevisiae SSN6 -LRB- also known as CYC8 -RRB- and S. castellii gene 705.55 are an example of single-copy paralogues -LRB- Fig. 1 -RRB- . This situation arises when opposite members of a gene pair are lost in two daughter species . Between S. cerevisiae and S. castellii , 176 of the 2,723 loci we surveyed -LRB- 6.4 % ; Classes 2E , 3A and 3B in Fig. 2 -RRB- show this pattern of reciprocal gene loss . Reciprocal gene loss is a particular form of reciprocal silencing1 or divergent resolution2 ,10 of duplicated genes , and is a property of a pair of genomes . Similarly , there are 198 reciprocal gene-loss loci between C. glabrata and S. castellii -LRB- 7.3 % -RRB- , and 100 between S. cerevisiae and C. glabrata -LRB- 3.7 % -RRB- . Thus , a significant minority of genes that are mutual best BLASTP hits between the post-WGD genomes are not orthologues . More importantly , the process of reciprocal gene loss has the effect of changing the location of the functional copy of a gene1 ,2 . For instance , S. castellii effectively carries a null allele at its locus orthologous to SSN6 , and S. cerevisiae has a null allele orthologous to gene 705.55 -LRB- Fig. 1 -RRB- . If this were the only difference between these two species and they formed a hybrid , the hybrid would be likely to have low fitness because onequarter of its spores would lack a functional copy of both SSN6 and gene 705.55 -LRB- S. cerevisiae ssn6 mutants are defective in respiratory growth and sporulation -RRB- . In fact , 66 of the 176 loci that have undergone reciprocal gene loss between S. cerevisiae and S. castellii involve essential S. cerevisiae genes , so the spore viability of the hypothetical hybrid is reduced to approximately -LRB- 0.75 -RRB- 66 or 6 1029 as a consequence of essential genes alone . Viability will be reduced further by reciprocal gene loss at loci that were not scored in Fig. 2 owing to inadequate synteny conservation -LRB- about half the genome -RRB- , and at loci such as SSN6 that are not essential but still contribute to fitness . The number of reciprocal losses observed among the postWGD species is ample to account for their reproductive isolation , Figure 2 | Classes of gene loss pattern among 2,723 ancestral loci in S. cerevisiae , S. castellii and C. glabrata , and their frequencies . Red marks denote gene absence and are used to group ancestral loci into 14 gene-loss classes , described by schematic trees showing the fates of orthologous and paralogous genes . The number of ancestral loci in each gene-loss class is shown in the centre of the tree . The two sets of species names in each tree denote tracks A and B in arbitrary order . In some cases , the absence of a gene copy in two or more species may be due to a single gene-loss event on a shared branch , but this does not affect classification . Convergent classes are those in which all genes lost are orthologues ; divergent classes involve some loss of paralogues in different species . 342 notwithstanding the contributions of mechanisms such as interchromosomal rearrangement11 ,12 and mismatch repair13 ,14 . The situation described above for the SSN6 and 705.55 genes is a special case of Bateson Dobzhansky Muller -LRB- BDM -RRB- interspecific genomic incompatibility15 . The BDM model proposes that negative epistatic interactions between two loci can reduce the fitness of a hybrid . Werth and Windham1 and Lynch and Force2 applied the BDM model to duplicated genes , hypothesizing that reciprocal loss -LRB- or silencing -RRB- of different copies in two species would create a BDM incompatibility , leading to reduced hybrid fitness . Reciprocal gene loss at multiple loci could lead to reproductive isolation , and where many duplicated genes exist -LRB- as in a polyploid -RRB- there is the potential for successive nested speciation events to occur1 ,2,10 . To investigate whether reciprocal gene loss was involved in the establishment of reproductive isolation among the post-WGD lineages , we determined the timing of gene losses by estimating the number of duplicated genes surviving at each node on the lineage leading to S. cerevisiae -LRB- Fig. 3 -RRB- . To increase the resolution of this analysis we included data from S. bayanus16 , a close relative of S. cerevisiae . -LRB- Reproductive isolation between these two species is due to processes other than reciprocal gene loss12 . -RRB- We expressed the ages of the nodes as a proportion of the time -LRB- T -RRB- since the initial divergence of gene pairs created by WGD -LRB- see Supplementary Notes S2 and S3 -RRB- . We then estimated the numbers of genes still duplicated in the common ancestors of S. cerevisiae and each of S. bayanus , C. glabrata and S. castellii using two methods : parsimony -LRB- which gives the minimum number of genes that must have been retained in duplicate -RRB- and a model-based approach -LRB- Supplementary Note S4 -RRB- . We consider the latter to be more realistic because it allows for parallel gene losses in different lineages . The parsimony and model-based methods both show a precipitous loss of duplicated genes in the time interval between WGD and the first speciation event -LRB- Fig. 3b , c -RRB- . Both methods also show that the fraction of genes retained in duplicate declined appreciably -LRB- from 47 % to 32 % according to the model-based method -RRB- in the interval between the first -LRB- S. castellii -RRB- and the second -LRB- C. glabrata -RRB- speciation , even though this corresponds to a very short time period . From this we conclude that gene loss was still occurring rapidly during the emergence of the post-WGD lineages . Moreover , because reciprocal gene loss -LRB- by definition -RRB- can not have occurred before S. castellii diverged from the other post-WGD lineages , and the number of gene losses on the right-hand side of the curve is very few -LRB- S. bayanus differs from S. cerevisiae at only two of the scored ancestral loci -RRB- , the vast majority of reciprocal losses must have occurred at around the time of the two speciation events . In fact , we estimate that two-thirds of all reciprocal gene-loss events occurred between the time of S. castellii divergence and time 0.337 T -LRB- Fig. 3b -RRB- . The reproductive barriers imposed on these species by reciprocal gene loss are therefore not recent reinforcements but were erected contemporaneously with speciation . The fate awaiting most gene pairs formed by WGD was that the duplication was resolved by deleting one gene copy -LRB- Fig. 2 -RRB- . If the two copies were functionally identical , we would expect the ` choice ' of which copy to delete to be arbitrary . This hypothesis can be tested at ancestral loci that have been resolved independently in more than one post-WGD lineage . We find that in cases of two independent losses , the two retained genes are more often orthologues than paralogues -LRB- in Fig. 2 , compare Class 2D to 2C , and 2F to 2E ; x2-test of homogeneity , P , 0.05 for each -RRB- . A possible explanation for the excess of convergent losses is that at some loci the two copies were not functionally identical , and that the same -LRB- betterfunctioning -RRB- copy was retained on both occasions . In contrast , the fact that divergent resolution is seen at some other loci suggests that the choice of survivor at those loci was arbitrary -LRB- Classes 2A , 2C , 2E and 3 -RRB- . These observations can be reconciled if some pairs of genes were functionally indistinguishable at the time the duplication was resolved -LRB- in which case either copy could be retained -RRB- but others were 2006 Nature Publishing Group NATURE | Vol 440 | 16 March 2006 LETTERS functionally distinct -LRB- so that a particular copy was preferred by selection -RRB- . Differences in the performance of a function can only have been due to sequence differences between the gene copies themselves or between their cis-regulatory regions . This sequence divergence must have accumulated in the time between WGD and gene loss , or , if the WGD was an allopolyploidy , have been inherited from parental species . Therefore , neutral gene loss -LRB- which results in divergent resolution half of the time -RRB- is expected to be more frequent at ancestral loci that are slowly evolving or involved in highly conserved biological processes where the potential for functional divergence is low . We tested this prediction and indeed find that loci in Class 3 -LRB- all of which underwent reciprocal gene loss between two species -RRB- evolve on average 30 % slower than Class 4 -LRB- for which no reciprocal gene loss occurred -RRB- -LRB- Supplementary Note S5 ; Wilcoxon rank-sum test P , 10214 -RRB- . Moreover , Gene Ontology terms such as ` ribosomal RNA processing ' , ` ribosome biogenesis ' and ` RNA binding ' are disproportionately over-represented among Class 3 loci , as are proteins that are localized to the nucleolus17 and proteins in complexes that bind RNAs18 -LRB- Supplementary Table S1 and Note S5 -RRB- . Finally , we also find that genes for small nucleolar -LRB- sno -RRB- RNAs , many of which function in ribosomal RNA processing , have undergone reciprocal gene loss unusually frequently -LRB- Supplementary Note S5 -RRB- . Thus , the set of reciprocal gene-loss loci seems biased towards those with functions most likely to be conserved between duplicates . This functional bias increases the potential contribution of reciprocal gene-loss loci to reproductive isolation , because 40 % of the Class 3 loci are essential19 in S. cerevisiae , compared with 20 % of Class 4 loci -LRB- P , 10210 , x2-test -RRB- . The passive loss of genes from genomes in which there is no selection to retain them is a familiar phenomenon in molecular evolution20 ,21 . We suggest that passive gene loss is the likely mechanism of the original WGD event in yeast . Our model -LRB- Fig. 4 -RRB- begins with two haploid cells fusing to form a diploid . If the haploids are from different species , or differ by a chromosomal rearrangement , or carry particular mutations , the resulting diploid may be unable to form viable spores but still able to divide mitotically . If the diploid cell lineage continues to divide mitotically for many generations , it can start to lose one allele from every locus that is not haploinsufficient . During this process , there is nothing to prevent an allele at the MAT locus from being deleted , in which case the cell will behave as a haploid . It can switch mating type , undergo mother daughter mating , form a diploid , and so regain fertility22 . Former alleles become separate loci , each of which is homozygous . Continuing loss of redundant gene copies will result in separate lineages that are self-fertile but reproductively isolated from one another by reciprocal gene loss -LRB- Fig. 4 -RRB- . Our results provide evidence that reciprocal gene loss at multiple ancestrally duplicated genes may lead to speciation , as has previously been hypothesized -LRB- but not demonstrated -RRB- for polyploid plants1 ,23 and fish10 ,24 . Indeed , because we have shown that reciprocal gene loss is implicated in the emergence of three different lineages , our data support the feature of the modified BDM mechanism1 ,2 that most distinguishes it from other theories of reproductive isolation : the ease with which it accounts for multiple speciation events . Finally , by showing that slowly evolving genes and those involved in very fundamental processes are the ones most likely to undergo reciprocal Figure 3 | Time course of duplicated gene loss following WGD . a , Tree reconstructed from 909 protein sequences using a constrained topology -LRB- Supplementary Note S2 -RRB- and branch-length estimation by maximum likelihood . The black dot indicates the initial divergence of duplicates created by WGD -LRB- Supplementary Note S3 -RRB- . b , Gene-loss curves estimated by the model-based method -LRB- open circles and solid curve ; Supplementary Note S4 -RRB- and by parsimony -LRB- black circles and dashed curve -RRB- . Filled grey circles are common to both methods and show the percentages of loci duplicated in S. cerevisiae and its common ancestor with S. bayanus . The horizontal scale represents the time from the initial divergence of duplicates created by WGD -LRB- 0T -RRB- to the present -LRB- 1T -RRB- and is derived from the tree in a assuming a molecular clock -LRB- Supplementary Note S3 -RRB- . Power-law curves were fitted to the data25 . Standard errors for x -LRB- all ,2 % ; omitted for clarity -RRB- and y values were estimated by bootstrapping . c , Numbers of genes lost on each branch leading to post-WGD species , as inferred by the model-based method . The current numbers of duplicates remaining in each post-WGD genome are shown in parentheses . All numbers refer to the 2,723 loci summarized in Fig. 2 . 343 2006 Nature Publishing Group LETTERS NATURE | Vol 440 | 16 March 2006 Figure 4 | Model of passive gene loss as a mechanism for WGD and establishment of reproductively isolated lineages . Individual steps are discussed in the main text . Ovals represent yeast cells . Genes are shown as red , green or blue boxes -LRB- except for the MAT locus , shown in purple -RRB- , and are arranged horizontally as chromosomes . Grey ` X ' symbols indicate genes that have been deleted . Roman numbering of chromosomes is used to indicate the parent of origin where relevant . Features relevant to each step are circled in yellow or orange . RGL , reciprocal gene loss . gene loss , our study leads to the conclusion that these genes , which individually are among the most conservative in the genome , may collectively be responsible for the most radical of evolutionary events . METHODS We used the YGOB engine3 to assess the status and syntenic conservation of loci in S. cerevisiae , S. castellii and C. glabrata . Each ancestral locus -LRB- that is , a column in Fig. 1 , corresponding to two genomic sites in post-WGD species and one site in pre-WGD species -RRB- was scored up to 18 times : on tracks A and B in each of the three post-WGD species , and comparing against each of the three pre-WGD genomes . On the basis of homology and syntenic context , the status of each of the six genomic sites in the post-WGD species was designated as one of -LRB- 1 -RRB- gene unambiguously present , -LRB- 2 -RRB- gene unambiguously absent , -LRB- 3 -RRB- gene present but with insufficient syntenic support , or -LRB- 4 -RRB- gene absent but with insufficient syntenic support . Loci were retained for further analysis if presence or absence could be determined unambiguously on both tracks in all three post-WGD species and if the scoring against all three pre-WGD genomes was not contradictory . This yielded reliable information for 2,723 ancestral loci , as summarized in Fig. 2 . The scoring protocol and our implementation are described in ref . 3 . We ignored a small number of ancestral loci for which one of the post-WGD species retained neither gene copy . S. bayanus was scored relative to the 2,723 ancestral loci in S. cerevisiae because their genomes are almost completely co-linear . In S. bayanus , 2,631 loci had conserved syntenic context -LRB- by the criteria above -RRB- , and manual inspection of candidates generated by the YGOB engine revealed just two differences between S. bayanus and S. cerevisiae -LRB- S. bayanus has retained paralogues as well as orthologues of HEK2 and YAT1 -RRB- . 344 Received 5 August ; accepted 22 December 2005 . 1 . 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Postlethwait , J. , Amores , A. , Cresko , W. , Singer , A. & Yan , Y. L. Subfunction partitioning , the teleost radiation and the annotation of the human genome . Trends Genet . 20 , 481 -490 -LRB- 2004 -RRB- . 25 . Maere , S. et al. . Modeling gene and genome duplications in eukaryotes . Proc . Natl Acad . Sci . USA 102 , 5454 -5459 -LRB- 2005 -RRB- . Supplementary Information is linked to the online version of the paper at www.nature.com/nature . Acknowledgements We are grateful to D. Bradley , G. Conant , J. Conery , B. Cusack , H. Fraser , N. Khaldi , W.-H . Li , A. Lloyd , A. McLysaght , G. Singer , R. Vilgalys and M. Woolfit for discussion . This study was supported by Science Foundation Ireland . Author Contributions S.W. and K.H.W. did pilot studies . K.P.B. developed the YGOB and the scoring scheme . D.R.S. did all analyses except those of gene function -LRB- K.P.B. -RRB- and chromosomal rearrangements -LRB- J.L.G. -RRB- . Manual editing of data in YGOB was done equally by all authors . D.R.S. and K.H.W. wrote the manuscript . Author Information Reprints and permissions information is available at npg.nature.com/reprintsandpermissions . The authors declare no competing financial interests . Correspondence and requests for materials should be addressed to K.H.W. -LRB- khwolfe@tcd.ie -RRB- . 2006 Nature Publishing Group 345

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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A gene atlas of the mouse and human protein-encoding transcriptomes Andrew I. Su * , Tim Wiltshire * , Serge Batalov * , Hilmar Lapp * , Keith A. Ching * , David Block * , Jie Zhang * , Richard Soden * , Mimi Hayakawa * , Gabriel Kreiman * , Michael P. Cooke * , John R. Walker * , and John B. Hogenesch * * The Genomics Institute of the Novartis Research Foundation , 10675 John J. Hopkins Drive , San Diego , CA 92121 ; and Department of Neuropharmacology , The Scripps Research Institute , 10550 North Torrey Pines Road , San Diego , CA 92037 Edited by Peter K. Vogt , The Scripps Research Institute , La Jolla , CA , and approved March 2 , 2004 -LRB- received for review February 3 , 2004 -RRB- The tissue-specific pattern of mRNA expression can indicate important clues about gene function . High-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale . Toward this end , we have designed custom arrays that interrogate the expression of the vast majority of protein-encoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues . The resulting data set provides the expression patterns for thousands of predicted genes , as well as known and poorly characterized genes , from mice and humans . We have explored this data set for global trends in gene expression , evaluated commonly used lines of evidence in gene prediction methodologies , and investigated patterns indicative of chromosomal organization of transcription . We describe hundreds of regions of correlated transcription and show that some are subject to both tissue and parental allele-specific expression , suggesting a link between spatial expression and imprinting . T he completion of the human and mouse genome sequences opened an historic era in mammalian biology . One common conclusion from these projects was the determination that mammals have only 30,000 protein-encoding genes -LRB- 1 , 2 -RRB- . Yet , despite the apparent tractability of this figure -LRB- earlier estimates were much higher -RRB- , to date all existing research has determined the function of only a fraction of these genes . Currently , only 15,000 human and 10,000 mouse genes are described in the literature -LRB- Medline , www.ncbi.nih.gov Pubmed -RRB- . The challenge and opportunity for genomics strategies and techniques are to accelerate the functional annotation of novel genes from the uncharted genome . High-throughput technologies for biological annotation have the capacity to partially address the discrepancy between the identification of genes and the understanding of their function . For example , proteins have well defined molecular roles encoded in their primary amino acid sequence as domains . Using sequence informatics , these domains can be used as a tool to search the entire genome to find protein family members that likely function in an analogous manner . Gene expression arrays have also been a useful tool for genome-wide studies where changes in gene expression can be associated with physiological or pathophysiological states -LRB- 3 -RRB- . Recently , other high-throughput techniques such as RNA interference -LRB- 4 -RRB- and cDNA overexpression -LRB- 5 -RRB- have been developed , further accelerating functional genome annotation . The integration of these diverse strategies is critical to annotation efforts and remains a significant challenge . Previously , we generated a preliminary description of the human and mouse transcriptome using oligonucleotide arrays that interrogate the expression of 10,000 human and 7,000 mouse target genes -LRB- 6 -RRB- . We explored this data set for insights into gene function , transcriptional regulation , disease etiology , and comparative genomics . However , this data set was based on commercially available gene expression arrays and therefore was biased toward previously characterized genes . In this report , we significantly extend this earlier work by determining the expresPNAS April 20 , 2004 vol . 101 no. 16 sion patterns of previously uncharacterized protein-encoding genes and de novo gene predictions from the mouse and human genome projects . Using custom-designed whole-genome gene expression arrays that target 44,775 human and 36,182 mouse transcripts , we have built a more extensive gene atlas using a panel of RNAs derived from 79 human and 61 mouse tissues . This data set constitutes one of the largest quantitative evaluations of gene expression of the protein-encoding transcriptome to date . Building on our previous analyses , these expression patterns were examined for global trends in gene expression . We also provide experimental validation of thousands of gene predictions and use these data to determine which of the commonly used types of evidence for gene prediction most accurately correlates with expressed genes . In addition , we used this data set to search for chromosomal regions of correlated transcription -LRB- RCTs -RRB- , which may indicate higher-order mechanisms of transcriptional regulation . Furthermore , we show that some of these tissue-specific coregulated genes are subject to another form of regulation , parental imprinting , and thus that several of these regions are under the control of both tissue - and parental allele-specific expression . Finally , we have made these data publicly available for searching and visualization by keyword , accession number , sequence , expression pattern , and coregulation at our web site -LRB- http : symatlas.gnf.org -RRB- . Materials and Methods target sequences for the human and mouse using the following sources : RefSeq -LRB- 15,491 human and 12,029 mouse sequences -RRB- -LRB- 7 -RRB- ; Celera -LRB- 49,859 human and 29,331 mouse sequences -RRB- -LRB- 8 -RRB- ; Ensembl -LRB- 33,698 human sequences -RRB- ; and RIKEN -LRB- 46,299 mouse sequences -RRB- -LRB- 9 -RRB- . First , all sequences were screened with REPEATMASKER -LRB- www.repeatmasker.org -RRB- to remove repetitive elements . Next , sequence identity between individual sequences was established by using pairwise BLAT -LRB- 10 -RRB- or BLAST -LRB- 11 -RRB- and SIM4 -LRB- 12 -RRB- . The results from single-linkage clustering were further triaged to produce a final target set of 44,775 human and 36,182 mouse targets with the highest degree of confidence of computational prediction -LSB- biasing toward sequences containing Interpro domains -LRB- 13 -RRB- and away from noncoding RNAs -RSB- . Finally , the human sequence set was pruned of all targets already represented on the Affymetrix -LRB- Santa Clara , CA -RRB- commercially available HG-U133A array , leaving 22,645 target sequences for our custom array . One hundred target sequences from the This paper was submitted directly -LRB- Track II -RRB- to the PNAS office . Abbreviations : RCT , regions of correlated transcription ; AUC , area under the curve ; LCR , locus control regions . A.I.S. , Microarray Chip Design . We identified a nonredundant set of T.W. , and S.B. contributed equally to this work . Present address : Center for Biological and Computational Learning , Massachusetts Institute of Technology , MIT E25-201 , Cambridge , MA 02142 . whom correspondence should be addressed . E-mail : hogenesch@gnf.org . To 2004 by The National Academy of Sciences of the USA 6062 6067 www.pnas.org cgi doi 10.1073 pnas .0400782101 HG-U133A chip were also included in the GNF1H design for the normalization procedure -LRB- see below -RRB- . The final human and mouse target sets were submitted to the Affymetrix chip design pipeline for fabrication of the GNF1H and GNF1M arrays , respectively . Tissue Preparation . Human tissue samples were obtained from the mouse and human data sets were used . All analyses and visualizations were performed by using R -LRB- www.r-project.org -RRB- . Imprinting Analysis . Allele-specific probe expression analysis was used to identify genes with an imprinted expression pattern . Two distinct mouse strains , C57BL 6J -LRB- B6 -RRB- and Mus musculus castaneus -LRB- CAST Ei -RRB- , were bred to produce four independent mouse crosses -LRB- male : : female -RRB- : B6 : : B6 , B6 : : CAST Ei , CAST Ei : : B6 , and CAST Ei : : CAST Ei . Each litter of embryonic day 14 16 embryos was pooled , and RNA from four to five separate litters was labeled and hybridized to GNF1M arrays . A probe-level analysis was performed to detect naturally occurring polymorphisms between the two strains . Individual probes -LRB- but not entire probe sets -RRB- showing a significantly different signal between the two homozygous groups were identified as putative polymorphisms in the target gene . Next , the hybridization signal from the two reciprocal crosses was examined for statistically significant differences in signal based on the paternal or maternal allele , as assessed by t test -LRB- P 0.001 -RRB- , indicating a pattern of male or female imprinting . several sources : Clinomics Biosciences -LRB- Pittsfield , MA -RRB- , Clontech , AllCells -LRB- Berkeley , CA -RRB- , Clonetics BioWhittaker -LRB- Walkersville , MD -RRB- , AMS Biotechnology -LRB- Abingdon , Oxfordshire , U.K. -RRB- , and the University of California at San Diego . When samples from four or more subjects were available , equal numbers of male and female subjects were used to make two independent pools ; when fewer than four samples were available , RNA samples were pooled , and duplicate amplifications were performed for each pool -LRB- detailed annotation for human samples is on our web site and in Table 1 , which is published as supporting information on the PNAS web site -RRB- . Adult -LRB- 10 - to 12-week-old -RRB- mouse tissue samples were independently generated from two groups of four male and three female C57BL 6 mice by dissection , and tissues were subsequently quickly frozen on dry ice . Tissues were pulverized while frozen , and total RNA was extracted with Trizol -LRB- Invitrogen , Carlsbad -RRB- by using 100 mg of tissue , then further processed by using the RNeasy miniprep kit according to manufacturer 's protocols -LRB- Qiagen , Chatsworth , CA -RRB- . The quality of all samples was determined with an Agilent Bioanalyzer -LRB- Palo Alto , CA -RRB- . Microarray Procedure . Microarray analysis was performed essentially as described -LRB- 14 -RRB- . Briefly , 5 g of total RNA was used to synthesize cDNA that was then used as a template to generate biotinylated cRNA . cRNA was fragmented and hybridized to Affymetrix custom or commercially available gene expression arrays . The arrays were then washed and scanned with a laser scanner , and images were analyzed by using the MAS5 algorithm -LRB- 15 -RRB- . Arrays were normalized by using global median scaling . The human HG-U133A and GNF1H chips , which were hybridized to the same biological sample , were first paired and prenormalized by using the common targets . The condensed data files are available from our web site -LRB- http : symatlas . gnf.org -RRB- and Gene Expression Omnibus -LRB- www.ncbi.nih.gov geo -RRB- -LRB- 16 -RRB- . Raw CEL files will be provided upon request -LRB- http : symatlas.gnf.org -RRB- . Identification of RCTs . All target genes were mapped to their corresponding genome assembly -LRB- human to National Center for Biotechnology Information Hs34 assembly , mouse to February 2003 Mm30 assembly -RRB- by using BLAT -LRB- 10 -RRB- . To account for multiple probes interrogating a single gene , target sequences were also compared to UniGene -LRB- www.ncbi.nih.gov UniGene -RRB- by using BLAST . Target sequences that map within 25 kb of each other and to a common UniGene cluster were pooled , and their expression values were averaged and treated as a single target in the RCT analysis . Next , each chromosome was scanned in window sizes of 3 10 adjacent genes . Windows where 50 % of all pairwise comparisons of expression pattern showed a Pearson correlation coefficient 0.6 were identified as RCTs . Randomization studies of gene order confirmed the significance of both the overall number of RCTs and the average pairwise correlation of each individual RCT -LRB- P 0.005 , correcting for multiple testing -RRB- . Pairwise sequence similarity within each RCT was assessed by using TBLASTX -LRB- 11 -RRB- , where a similarity value is the product of the alignment similarity and the percentage of total sequence length aligned . Synteny between the human and mouse genome assemblies was derived from a published analysis of syntenic anchors -LRB- 17 -RRB- . For the analysis of evolutionarily conserved RCTs , only the 32 tissues profiled in common between Su et al. . Results and Discussion The tissue-specific RNA expression pattern of a gene can indicate important clues to its physiological function . To build an extensive atlas of tissue-specific gene expression , we created custom arrays that interrogate the expression of known and predicted protein-encoding genes from the mouse and human genomes . The design process used a nonredundant set of known genes and gene predictions compiled from Refseq , Celera , Ensembl -LRB- for human -RRB- , and RIKEN -LRB- for mouse -RRB- . For our GNF1H custom human array , we further removed gene targets that were already represented on the commercially available HG-U133A array from Affymetrix . Finally , we biased the final selection toward gene predictions with likely protein-coding regions . In total , the U133A GNF1H chipset interrogates 44,775 probe sets , and our custom-designed GNF1M mouse array interrogates 36,182 probe sets . As of the most current annotation in January 2004 , these correspond to 33,698 and 33,825 unique human and mouse genes , respectively , after accounting for multiple probe sets interrogating single genes and split transcripts . Using these whole-genome gene expression arrays , we measured the expression of an extensive set of transcripts and transcript predictions on a single technology platform across a diverse panel of 79 human and 61 mouse tissues . This gene atlas represents the normal transcriptome and allowed us to examine global trends in gene expression . Classical reassociation kinetics -LRB- Rot -RRB- has been used to assess global trends in gene expression at a population level -LRB- 18 -RRB- . The analysis of our data set expands this knowledge by examining transcript expression across a large number of tissues at the individual transcript level . We find that 52 % -LRB- 16,454 -RRB- and 59 % -LRB- 17,924 -RRB- of target genes are detected in at least one tissue in the human and mouse , respectively -LRB- Fig. 4A , which is published as supporting information on the PNAS web site -RRB- . The average number of transcripts expressed in a single tissue was 8,200 -LRB- mouse -RRB- . These observations generally concur with previous findings derived from Rot analyses , which indicate that 10,000 15,000 mRNAs are expressed in a given tissue at 1 10 copies per cell , and that 90 % of these are common between two tissues -LRB- 19 -RRB- . However , although Rot analysis suggests that the majority of transcripts are present in many or all tissues , our data show that 1 % of human target sequences are ubiquitously expressed . Approximately 3 % of mouse target sequences are detected in all samples profiled , although this number will certainly decline as the number of samples increases . Not surprisingly , the expression of these ubiquitously expressed housekeeping genes is 30-fold higher than for all genes in the data set -LRB- Fig. 4B -RRB- . PNAS April 20 , 2004 vol . 101 no. 16 6063 GENETICS Fig. 1 . Validation of gene predictions in humans . Gene targets on the GNF1H array were divided into four categories : contained in Refseq , predicted by both gene prediction efforts considered -LRB- `` Consensus '' -RRB- , and predicted by only one group -LRB- `` Ensembl-only '' and `` Celera-only '' -RRB- . On the left axis -LRB- solid bars -RRB- , rates of validation are shown , where detectable expression in at least one tissue is taken as evidence of the validity of a gene prediction . The right axis -LRB- blue line -RRB- indicates the total number of validated genes per group . Another valuable use of this data set is characterization of novel predicted genes derived from the mouse and human genome projects -LRB- 1 , 2 -RRB- . Many of these exist solely as in silico predictions , and therefore evidence of their expression can serve as validation of these predictions . Furthermore , determining the expression pattern of an uncharacterized gene can indicate the appropriate tissue -LRB- s -RRB- from which the transcript can be cloned , as well as provide a base layer of physiological annotation . Gene prediction is an inexact art , where distinct methods and researchers often produce largely nonoverlapping sets of gene predictions -LRB- 20 -RRB- . For the human data , we subdivided the transcripts into four classes based on annotation information at the time of design : known genes found in Refseq , genes predicted independently by two groups -LRB- Celera and Ensembl -RRB- , singleton predictions found by the Ensembl group only , and singleton predictions found by the Celera group only . As expected , the set of known genes -LRB- Refseq -RRB- has the highest rate of detection in our data set , because 79 % have detectable expression in at least one sample -LRB- Fig. 1 -RRB- . Because all Refseq genes are known to be expressed , this suggests that our methodologies and current tissue libraries have a minimum false-negative rate of 21 % in detection of expression . This can certainly be improved with the profiling of additional tissues and cell types . Consensus gene predictions have a higher rate of detectable expression -LRB- 53 % -RRB- than either singleton gene predictions offered by Ensembl or Celera only -LRB- 30 % and 24 % , respectively -RRB- -LRB- Fig. 1 -RRB- . Although the Ensemblonly group had a slightly higher rate of detection , a greater total number of Celera-only predictions was detected -LRB- 2,918 Celera vs. 618 Ensembl predictions -RRB- . Analogous results are seen in the mouse data set , in which Refseq genes had a higher rate of detection than gene predictions by Celera -LRB- 79 % vs. 46 % -RRB- . The differences among these three classes are also reflected in the quantitative measures of gene expression . On average , human Refseq genes are expressed at a level 2-fold higher than consensus predictions , which in turn are 66 % higher than singleton predictions -LRB- P 0.001 ; data not shown -RRB- . This observation likely reflects a historical bias in the biology of studying highly abundant proteins . In total , we find evidence of expression for 5,641 -LRB- 31.2 % -RRB- human and 2,629 -LRB- 46.2 % -RRB- mouse gene predictions through detection of their transcribed mRNA product in at least one tissue . In addition , we describe the expression pattern for 9,708 mouse RIKEN-derived genes , many of which lack significant expression annotation . It is important to note that the gene 6064 www.pnas.org cgi doi 10.1073 pnas .0400782101 predictions for which we do not observe detectable expression are not necessarily incorrect , because the appropriate tissue -LRB- s -RRB- for a given gene may have not been profiled , the gene may be present in a small number of copies -LRB- e.g. , in a small subset of cells within a tissue -RRB- , or the probe set may not properly interrogate the expression of the gene -LRB- e.g. , UTRs , split transcripts , or missing or mistaken terminal exons -RRB- . Despite these caveats , this data set provides the expression pattern of thousands of gene predictions and poorly characterized transcripts from the mouse and human genome projects , offering the opportunity to study the function of these genes in their most relevant tissues . Given the differing methods and subsequent results from gene prediction efforts , we next investigated which characteristics of a predicted transcript were better indicators of its detectable expression . In the methodology used by Celera , the following lines of evidence were considered in their gene prediction algorithm : `` conservation between mouse and human genomic DNA , similarity to human -LSB- and -RSB- rodent transcripts -LRB- ESTs and cDNAs -RRB- , and similarity of the translation of human genomic DNA to known proteins '' -LRB- 1 -RRB- . Using the detectable expression of a gene product as validation of the prediction , we created receiver operating characteristic curves for each line of evidence that plot the true positive rate as a function of the false positive rate . The area under the curve -LRB- AUC -RRB- measures the strength of the predictor ; a perfect predictor would have AUC 1 , and a random factor would have AUC 0.5 . When comparing the predictor strength among the three lines of evidence above in the human data set , we find that although no single line of evidence is universally predictive of expression , EST evidence has the most predictive value -LRB- AUC 0.77 -RRB- -LRB- Fig. 5 , which is published as supporting information on the PNAS web site -RRB- , an observation likely linked to the fact that highly expressed genes are more likely to be represented in EST databases . Protein homology support and sequence similarity between human and mouse genomic sequences both had a lesser impact on the validation of gene predictions -LRB- AUC of 0.66 and 0.65 , respectively -RRB- . The availability of additional mammalian genome sequences should increase the power of sequence conservation in gene prediction . Somewhat surprisingly , simply the length of the transcript prediction was also a reasonable predictor of detection in our data set -LRB- AUC 0.68 -RRB- , suggesting that incomplete transcript predictions were significant factors in the nonobservation of many gene targets . We and others have used gene expression information , genome sequence , and de novo motif discovery tools to search for enhancer elements that direct tissue-specific gene expression -LRB- 21 , 22 -RRB- . In contrast to enhancers that generally direct the expression of a single gene , locus control regions -LRB- LCR -RRB- are characterized by their ability to promote the expression of multiple genes at a single locus . To date , only a handful of LCRs have been reported -LRB- 23 -RRB- . Recently , Spellman and Rubin -LRB- 24 -RRB- used Drosophila gene expression arrays to identify 200 clusters of adjacent and similarly expressed genes and suggest that these patterns are most consistent with regulation of chromatin structure . Others -LRB- 25 27 -RRB- have also performed similar analyses in humans , Caenorhabditis elegans , and yeast on more limited sets of experimental conditions . To identify potential loci in our data set , the expression of which may be controlled in a locus-dependent manner , we mapped the transcripts represented on our gene expression arrays to genome assemblies and scanned each chromosome for windows of genes with correlated expression patterns . We called these sites RCTs as a general term encompassing LCRs and correlated expression achieved through gene duplication . It is important to note that detection of these RCTs is heavily influenced by comparison algorithms , normalization procedures , and underlying data . In particular , the inclusion of several purified immune cell populations in our human sample set Su et al. . Fig. 2 . RCT . -LRB- A -RRB- An RCT was identified on mouse chromosome 9 , consisting of 11 genes that share a highly conserved expression pattern . -LRB- Upper -RRB- The y axis is average normalized expression value , the x axis contains the 61 different tissues , and red bars are fertilized egg and oocytes . The correlation plot -LRB- Lower Left -RRB- visualizes the pairwise correlation coefficients . Each row represents a gene , ordered vertically according to their position on the chromosome . The center yellow vertical strip represents autocorrelation -LRB- R 1 -RRB- ; positions to the right of center represent correlation of the gene to its downstream neighbors , whereas positions to the left represent correlation to the upstream neighbors . Yellow indicates high correlation ; blue indicates low correlation -LRB- scale at bottom -RRB- . The sequence similarity plot -LRB- using TBLASTX , Lower Right -RRB- has the same structure as the correlation plot , except pairwise sequence similarity is shown . In this RCT with high expression levels in fertilized eggs and oocytes , the genes share a high degree of sequence similarity , likely indicating they are all members of a single gene family and the result of one or more gene duplication events . -LRB- B -RRB- An example RCT is identified on human chromosome 13 , which contains three genes with highly correlated expression -LRB- red bars are brain regions , green bar is fetal brain -RRB- . In contrast to the first example , these genes share very little pairwise sequence similarity . -LRB- C -RRB- An evolutionarily conserved RCT is shown from human chromosome 2 -LRB- Left -RRB- and the syntenic region on mouse chromosome 6 -LRB- Right -RRB- . These RCTs share a pancreas-enriched expression pattern -LRB- red bar -RRB- , as well as significant sequence similarity . skewed the normalization procedure and led to an increase in RCTs whose expression is enriched in these samples . In total , we identified 156 and 108 RCTs in human and mouse , respectively -LRB- descriptions of all RCTs are available for download from http : symatlas.gnf.org -RRB- . Tissues with very specific clusters of genes such as those in the immune system , liver , testis , and placenta had more RCTs than other tissues in both the mouse and human data sets . Mechanistically , expression of these RCTs is likely mediated through either common promoter elements -LRB- resulting from gene duplication -RRB- or through higher-order gene regulation such as site-specific chromatin remodeling . To separate these two possibilities , we identified likely paralogs using TBLASTX , a local six-frame translated nucleotide-to-nucleotide alignment algorithm -LRB- 11 -RRB- . RCTs whose genes share significant sequence similarity in their coding sequences are likely to be products of gene duplication , whereas dissimilar genes may result from an LCR or other higher-order transcriptional regulation . As expected , we found RCTs with both related and unrelated genes . Fig. 2A illustrates an example of an RCT driven by gene duplication . This cluster of genes on mouse chromosome 9 represents a family of 11 uncharacterized F-box and WD40 repeat containing proteins that are specifically expressed in fertilized eggs and oocytes . Because of their high degree of sequence similarity , we hypothesize that their correlated expression pattern is a result of duplicated regulatory elements present Su et al. . in their structural genes , and that these genes may play an important role in the specialized protein expression of oocytes . In contrast , we also note a cluster of three genes with no apparent sequence similarity on human chromosome 13 that are highly enriched in samples derived from brain tissues , particularly the fetal brain sample -LRB- Fig. 2B -RRB- . The genes in this cluster are neurobeachin , an uncharacterized mRNA , and doublecortinand calmodulin kinase-like 1 protein -LRB- DCAMKL1 -RRB- . It is appealing to hypothesize that the correlated expression patterns of these genes and their colocalization at a chromosomal locus indicate a common role in a neurological process or network . Because these genes do not share sequence similarity , this region may also contain a previously unrecognized LCR or strong regional enhancer . Overall , 97 -LRB- 62 % -RRB- and 78 -LRB- 72 % -RRB- of the human and mouse RCTs identified have an average pairwise sequence similarity of 20 % and do not encode related genes . We next examined both the mouse and human data for RCTs that were identified in both data sets and are likely evolutionarily conserved . The majority of the RCTs were not found in both human and mouse , in many cases because the orthologs or syntenic regions have not yet been defined or the patterns were not conserved . However , in some cases , the apparent lack of conservation likely reflects physiological differences between the two organisms . For example , we observed RCTs with expression enriched in the olfactory bulb present in the mouse PNAS April 20 , 2004 vol . 101 no. 16 6065 GENETICS Fig. 3 . Six genes on mouse chromosome 12 share a distinctive pattern of expression . -LRB- A -RRB- A genomic view of this region -LRB- not to scale -RRB- . Locations of the genes on the mouse genome assembly : Dlk -LRB- 103.508 Mb -RRB- , Gtl2 -LRB- 103.593 Mb -RRB- , 1110006E14Rik -LRB- 103.646 Mb -RRB- , Rian -LRB- 103.696 Mb -RRB- , 5330411G14Rik -LRB- 103.788 Mb -RRB- , C130007E11Rik -LRB- 103.798 Mb -RRB- , and Dio3 -LRB- 104.328 Mb -RRB- . -LRB- B -RRB- These genes share enriched expression in brain regions -LRB- green bars -RRB- and umbilical cord -LRB- red bar -RRB- . The y axes indicate normalized expression values , whereas each bar along the x axis indicates a sample profiled in our data set . -LRB- C -RRB- Three of these genes -LRB- Dlk1 , Gtl2 , and Rian -RRB- have been previously reported to be imprinted . Using our allele-specific probe expression analysis approach -LRB- see text -RRB- , we confirmed the imprinted regulation of Gtl2 and Rian and report two previously undescribed imprinted transcripts at this locus -LRB- 5330411G14Rik and C130007E11Rik -RRB- . The y axes indicate the normalized signal intensity for individual probes on the array , and each bar represents a pooled sample from a cross indicated by color -LRB- see key -RRB- . but not the human data set . Nevertheless , several RCTs were conserved , including a cluster of pancreas-specific genes mapping to human chromosome 2 and its syntenic region on mouse chromosome 6 -LRB- Fig. 2C -RRB- . The human cluster is comprised of five genes , including pancreatitis-associated proteins -LRB- PAP -RRB- , three regenerating islet-derived proteins -LRB- REG1A , REG1B , and REGL -RRB- , and one protein of unknown function -LRB- LPPM429 -RRB- . The mouse cluster contains the ortholog to PAP , four isoforms of regenerating islet-derived proteins , and islet neogenesisassociated protein-related protein . The conservation of this RCT in human and mouse suggests that these genes perform analogous and important roles in both of these mammals . After mapping all target genes to their respective genome assemblies , we noted a region of mouse chromosome 7 -LRB- 130 Mb -RRB- that contained several genes previously shown to be imprinted -LRB- 28 30 -RRB- , three of which -LRB- H19 , Igf2 , and Cdkn1c -RRB- shared a pattern 6066 www.pnas.org cgi doi 10.1073 pnas .0400782101 of enriched expression in placenta , umbilical cord , and embryonic tissues . We also noted another pair of adjacent genes -LRB- Zim1 and Peg3 -RRB- elsewhere on chromosome 7 -LRB- 6 Mb -RRB- that shared this tissue-specific expression pattern , and whose expression has been shown to be imprinted -LRB- 31 -RRB- . Prompted by these observations , we examined our set of RCTs for other imprinted genes that were clustered in a single locus . On mouse chromosome 12 -LRB- 103 Mb -RRB- , we observed an RCT that consists of six adjacent genes , all with enriched expression in brain regions and umbilical cord -LRB- Fig. 3 A and B -RRB- . Recently , several groups showed that two genes in this locus , Dlk1 and Gtl2 , are imprinted -LRB- reviewed in ref . 32 -RRB- . Later , it was also shown that another gene at this locus , Rian , and several adjacent tandemly repeated C D small nucleolar RNA genes are also imprinted -LRB- 33 , 34 -RRB- . Furthermore , although we do not have a probe set on our array that reliably detects its expression , Dio3 is located proximal to this locus and has also Su et al. . shown to exhibit genomic imprinting -LRB- 35 -RRB- . The imprinting status of the three remaining R IKEN clones at this locus -LRB- 1110006E14Rik , 5330411G14Rik , and C130007E11Rik -RRB- is not known , although they share the brain - and umbilical cordenriched expression characteristic of all of the genes in the RCT . To investigate whether these three genes were also imprinted , we used two distinct mouse strains , C57BL 6J -LRB- B6 -RRB- and M. m. castaneus -LRB- CAST Ei -RRB- , to set up four independent mouse crosses -LRB- male : : female -RRB- : B6 : : B6 , B6 : : CAST Ei , CAST Ei : : B6 , and CAST Ei : : CAST Ei . Four independent litters of pooled embryonic day 14 16 embryos were dissected , and RNA expression was analyzed by allele-specific probe expression analysis , which allows us to determine whether the transcript is expressed exclusively or preferentially from either the paternal or maternal allele . This analysis reconfirmed the imprinted expression of Gtl2 and Rian -LRB- Fig. 3C -RRB- . Because no probes could distinguish between the B6 and CAST Ei forms of Dlk1 , we were unable to reconfirm its imprinted expression . Two of the uncharacterized R IKEN genes at this locus , 5330411G14Rik and C130007E11Rik , showed expression from the maternal allele only , further expanding the number of known imprinted genes at this locus -LRB- Fig. 3C -RRB- . Because these cDNAs are within 10 kb of one another , it is possible they are derived from the same structural gene . The third gene -LRB- 1110006E14Rik -RRB- , like Dlk1 , did not contain a probe capable of ascertaining its imprinting status . During the preparation of this manuscript , another gene in this locus sharing the 3 - end of C130007E11Rik was also shown to be imprinted -LRB- 36 -RRB- . In sum , the allele-specific probe expression analysis method has identified another two imprinted transcripts at this locus . 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Georges , M. , Charlier , C. & Cockett , N. -LRB- 2003 -RRB- Trends Genet . 19 , 248 252 . 33 . Hatada , I. , Morita , S. , Obata , Y. , Sotomaru , Y. , Shimoda , M. & Kono , T. -LRB- 2001 -RRB- J. Biochem . 130 , 187 190 . 34 . Cavaille , J. , Seitz , H. , Paulsen , M. , Ferguson-Smith , A. C. & Bachellerie , J. P. -LRB- 2002 -RRB- Hum . Mol . Genet . 11 , 1527 1538 . 35 . Yevtodiyenko , A. , Carr , M. S. , Patel , N. & Schmidt , J. V. -LRB- 2002 -RRB- Mamm . Genome 13 , 633 638 . 36 . Seitz , H. , Youngson , N. , Lin , S. P. , Dalbert , S. , Paulsen , M. , Bachellerie , J. P. , Ferguson-Smith , A. C. & Cavaille , J. -LRB- 2003 -RRB- Nat . Genet . 34 , 261 262 . Su et al. . PNAS April 20 , 2004 vol . 101 no. 16 6067 GENETICS ther augmentation by additional samples , including region-specific dissections using laser capture microdissection or even cell typespecific gene expression , will undoubtedly increase the utility of these resources . We have investigated this data set for global signatures in tissue-specific gene regulation , expression characteristics of de novo predicted transcripts , and chromosomal RCTs . The identification of several known imprinted loci in our tissue-specific RCT list suggests that these regulatory mechanisms that direct tissue - or parental allele-specific expression may be intertwined . Consistent with this observation , we were able to identify two previously undescribed transcripts that were imprinted on mouse chromosome 12 based on the observation that they share a tissuespecific expression pattern with their neighbors . With the sequencing phase of the human and mouse genome projects nearly complete , and with the rapid progress in the sequencing of other mammalian genomes , we are now poised to develop and exploit a variety of methods to ascertain the function of the thousands of recently described genes . In this regard , the genome-scale RNA expression data described herein provide a framework for the functional annotation process . By making the underlying data available on our web site -LRB- http : symatlas.gnf.org -RRB- and through the Gene Expression Omnibus -LRB- www.ncbi.nih.gov geo -RRB- , we anticipate that this study will aid researchers throughout the global research community to reap the harvests of the human and mouse genome projects .

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