Caenorhabditis elegans offer an opportunity to learn more about human gene function through functional characterization of orthologs in the worm. Based on a previous genome-wide analysis of worm-human orthologous transmembrane proteins, we selected seventeen genes to explore experimentally in C. elegans. These genes were selected on the basis that they all have high confidence candidate human orthologs and that their function is unknown. We first analyzed their phylogeny, membrane topology and domain organization. Then gene functions were studied experimentally in the worm by using RNA interference and transcriptional gfp reporter gene fusions.The complete genome sequences for human and the nematode sft-4, the worm ortholog of surfeit locus gene Surf-4, proved to be an essential gene required for development during the larval stages of the worm. R155.1, whose human ortholog is entirely uncharacterized, was implicated in body size control and other developmental processes.The experiments gave functional insights for twelve of the genes studied. For example, C36B1.12, the worm ortholog of three presenilin-like genes, was almost exclusively expressed in head neurons, suggesting an ancient conserved role important to neuronal function. We propose a new transmembrane topology for the presenilin-like protein family. C. elegans experiments on orthologs, we provide functional insights on twelve previously uncharacterized human genes.By combining bioinformatics and  Caenorhabditis elegans has been used as a simple model for understanding animal biology for nearly four decades. After the sequencing of entire genomes from several metazoans, we are now in an excellent position to take a gene-centric approach to the worm as a model organism. A majority of human genes have homologs in C. elegans. In a comparative proteomics study, 83% of the worm proteome was found to have human homologous genes [The nematode us genes . Only 11us genes -4.C. elegans genome were classified and the human orthologs identified [In a previous study, all transmembrane protein families in the entified . PredictC. elegans orthologs to human genes experimentally in the worm, one can potentially learn more about the gene function also in humans. Depending on whether duplication(s) have occurred in one or both lineages since the speciation event, orthologs can form one-to-one, one-to-many or many-to-many relationships.Orthologs are sequences that arose from a common ancestor gene and were separated by a speciation event . IdentifParalogs arise from a duplication event. A common scenario when genes are duplicated is that one of the gene copies is under negative selective pressure and therefore retains the function of the ancestor. The other copy might then be more free to evolve a new function different from the ancestral function. This is the reason why paralogs in different species are less likely to share functionality compared to orthologs. Paralogs can be divided into two subtypes – outparalogs and inparalogs . OutparaC. elegans of seventeen genes. The criteria for selecting these genes were that they are high confidence candidate orthologs to human genes [Here we present an initial functional characterization in an genes  and thatC. elegans proteins, and two different methods were used to predict signal peptides (see Methods for details). Each predicting method has some margin of error; therefore the consensus from several different predictors is more likely to give a better estimate of the true topology. Results were viewed using the SFINX tool [The consensus of nine different methods was used to predict membrane topologies for the putative INX tool ,9, an exC. elegans genes, additional human orthologs have emerged from the sequencing efforts. At present, 29% (5 of 17) of the worm genes have one-to-many ortholog relationship with human genes, which means that there has probably been an expansion in the human lineage. This is the case for C36B1.12 (Q93346) and ZK721.1 (Q9GYF0)  , suggest that H13N06.5 is the putative ortholog to Q92504, whereas T28F3.3 may be an outparalog.The bootstrap support for the speciation node between T04A8.12 and its human ortholog Q9UHJ9 barely made the cutoff of 50% when PAM distances was used. With observed divergence and Poisson correction as distance methods, the bootstrap support improved to 99% and 67%, respectively. When analyzing the phylogenetic tree using Orthostrappper, there was a very strong support for this orthology assignment (94%). Therefore, we conclude that T04A8.12 is probably the ortholog to Q9UHJ9.C30H6.2 has three potential human orthologs, Q9H6T8, Q9NXC4 and Q96NN4. The bootstrap support for the speciation node with PAM distances was 57%. This improved to 87% and 88% with observed divergence and Poisson correction, respectively. Orthostrapper results showed a strong support for Q9H6T8 and Q9NXC4 as orthologs to C30H6.2 (84%), whereas the support for Q96NN4 was weaker (57%). Considering these results, we believe that all three human genes are orthologs to the worm gene; however, the ortholog relationship seems to be weaker between Q96NN4 and C30H6.2.The domain organization of the predicted proteins was analyzed using the Pfam database ,12 (see Drosophila melanogaster, mouse and human (Q9V4C6, Q8BFU1 and Q9NUM3, respectively). The other four worm genes appear to belong to the LIV-1 subfamily. This subfamily has a unique metalloprotease motif that raises the possibility that they might have protease activity [Five of the proteins (C30H6.2, T11F9.2, H13N06.5, T28F3.3 and T01D3.5) may have a PF02535 domain, which is annotated as being a ZIP domain. The ZIP family is believed to include zinc and other metal transporters. The ZIP proteins have been classified into four groups based on sequence conservation [activity . Within C36B1.12 was predicted to have a PF04258 domain, a probable signal peptide peptidase (SPP) domain. SPP catalyzes intramembrane proteolysis of some signal peptides after they have been cleaved from a preprotein. This processing by SPP is related to protein cleavage by presenilins. Homologs to SPP are divided into five subfamilies based on phylogenetic analysis (subfamily SPP and subfamilies SPPL1-4, for SPP like) . C36B1.1F40F9.1 and F40F9.2 seem to have a PF01027 domain (UPF0005), which is an uncharacterized protein family. Both F08F1.7 and ZK858.6 may belong to the PF02990 domain family (EMP70). Proteins in this family might be located to endosomal membranes . F14F3.3sft-4 and R155.1 exhibited phenotypes when both the N2 (wildtype) and the RNAi sensitive rrf-3(pk1426) II [rrf-3, although the Dpy (dumpy) phenotype seen for R155.1 was still low penetrant and relatively weak. The Lva  observed for sft-4 occurred at larval stages L2–L3 and there was an almost complete penetrance with the sensitive strain. The RNAi phenotypes for both genes were detected at all temperatures, although, for sft-4 they were more severe at higher temperatures. The positive results were verified with RNAi by injection in strain N2.Out of the seventeen genes studied, 1426) II ,20 straigfp were established for fourteen genes and the resulting gene expression was analyzed. The results are presented in Table Transcriptional fusions with sft-4 and ZK721.1 both during larval and adult stages  proteins . Presenilins are an important group of proteases acting in the nervous system. Abnormal proteolytic cleavage may result in accumulation of pathogenic insoluble proteins, implied in e.g. Alzheimer's disease. We have shown that C36B1.12 is probably expressed in head neurons and intestine; however, the intestinal expression might be ectopic (see Discussion for details), which would imply that the gene is exclusively expressed in neurons. This suggests that the three human orthologs may also encode neuronal functions.The three putative human orthologs (Q8TCT7, Q8TCT8 and Q8IUH8) to the The membrane topology of human proteins belonging to the presenilin-like family has been analyzed previously. Q8IUH8 was predicted to have seven transmembrane (TM) segments and a cytoplasmic C-terminus , and theMerging these two proposed topologies by accepting all TM segments predicted by one or the other study would yield a topology with nine TM segments. Our own analysis of the proteins in question using the SFINX tool ,9 providWe further analyzed the other members of this family with SFINX ,9, and cF40F9.1 and F40F9.2 are 48% identical to each other on the protein sequence level and they also appear to have similar membrane topologies. They are close in the genome (<100 bp apart), but on opposite strands. It has been shown that genes closer than 500 bp on opposite strands are likely to have a shared control region , which msft-4 gene (C54H2.5) is highly conserved throughout evolution with orthologs in both vertebrates and non-vertebrates. All orthologous relationships are one-to-one with a high bootstrap support. The tree in Fig. sft-4. O45731 (T02E1.7) was found to be 33% identical to sft-4 on the protein sequence level and it was also predicted to have a PF02077 (SURF4) domain. However, our RNAi screen indicates that there is no or little functional redundancy between the two genes, since sft-4 has a very strong RNAi phenotype, showing an almost complete larval arrest at stages L2–L3. The RNAi phenotype for T02E1.7 is wildtype according to previous studies [sft-4 . SID-1 has been identified as a protein that is required for systemic RNAi [sid-1 has been found in Drosophila, which can explain the observed absence of systemic RNAi in this organism [sid-1 is a probable outparalog to the human genes  [ZK721.1 is most probably orthologous to the human genes Q9NXL6 and Q9Y357 (CGI-40 protein). The CGI-40 protein was found in a screen where novel human genes evolutionary conserved in mic RNAi ,26. It wmic RNAi . The higorganism ,29. Our see Fig. . The tre studies ,31. FurtSLC39A4 (Q9H6T8) was identified as one of three possible human orthologs to C30H6.2. The human gene has been implicated in the rare inherited condition acrodermatitis enteropathica, which results from a defect in the absorption of zinc . It is bC. elegans has been shown to be a zinc transporter localized to intracellular membranes [Drosophila ortholog to H13N06.5 is Catsup (Catecholamines up, Q9V3A4), which encodes a negative regulator of tyrosine hydroxylase (TH) activity [Arabidopsis thaliana gene IAR1 (Q9M647) is also an ortholog to H13N06.5. It is proposed to be involved in auxin metabolism or response [IAR1 was shown to functionally substitute for the Arabidopsis gene. These data indicate that there is functional conservation among these orthologs and it is likely that H13N06.5, and possibly also T28F3.3, could play similar roles in the corresponding pathways in the worm.The predicted human ortholog (Q92504) to H13N06.5 in embranes . Q92504 activity . TH is aresponse . InteresBIGM103 (Q9C0K1) was identified as one of four candidate human orthologs to T11F9.2. The human gene was found to be induced during the infection and inflammatory response. It was also shown to play a role in intracellular zinc ion accumulation and retention . ConsequThe human ortholog to T01D3.5 has no known putative function. We observed a wildtype RNAi phenotype for T01D3.5 as well as for the other four PF02535 (ZIP) domain containing putative proteins (C30H6.2, H13N06.5, T11F9.2 and T28F3.3). This indicates that there might be some functional redundancy between these genes. Considering the information available, it is conceivable that T01D3.5 may also be a zinc/metal transporter.F08F1.7 and ZK858.6 show 47% identity on the protein sequence level and they have similar membrane topologies with a large N-terminal non-cytoplasmic region and nine transmembrane regions in the C-terminal part. F08F1.7 and one of the splice variants of ZK858.6 were predicted to have a N-terminal signal peptide. The phylogenetic analysis indicated that the human gene p76 (Q99805) is the ortholog to F08F1.7 and it appears to localize to endosomes . The funtth-1 (F08F1.8, O17389). TTH-1 is likely to belong to the PF01290 domain family (thymosin beta-4), which includes actin-binding proteins, implicating a possible role in cytoskeleton organization. ZK858.6 is predicted to be in an operon with ZK858.5 (Q94421) and ZK858.7 (Q94416). A PF05154 (TM2) domain with unknown function is likely to be present in ZK858.5. ZK858.7 might have a PF04189 domain , suggesting that it could be involved in translation. T04A8.12 may be in an operon with T04A8.11 (Q22140) and T04A8.13 (Q22142). T04A8.11 might be a ribosomal protein, since it appears to have a PF00252  domain. T04A8.13 was predicted to have a PF00646 (F-box) domain, which is known for forming structural complexes with other proteins. There are no matching Pfam-A domains for T04A8.12. The best candidate human ortholog to T04A8.12 is FRAG1 (fibroblast growth factor receptor activating gene 1, Q9UHJ9). FRAG1 seems to be ubiquitously expressed in adult human tissues and it has also been detected in several human tumor cell lines [C. elegans, suggesting an important biological role. Previous studies have shown that the human ortholog p76 (Q99805) is ubiquitously expressed [C. elegans EMP70 domain containing protein Y41D4A.4 (Q95Y24). We found that Y41D4A.4 is homologous to F08F1.7 and ZK858.6, and most likely an ortholog to the human gene Q9HD45. Taken together, these findings point to that F08F1.7, ZK858.6 and T04A8.12 might play fundamental biological roles, and that they may be involved in processes such as cellular organization (F08F1.7) and translation (ZK858.6 and T04A8.12).Both F08F1.7 and ZK858.6 are predicted to be in operons, as is T04A8.12 . F08F1.7ll lines . Our resxpressed . There wThe human genes Q99908 (BB1) and Q96N66 are probable orthologs to F14F3.3. BB1 has been shown to be overexpressed in breast and bladder carcinoma , suggestDrosophila ortholog to R155.1 is Nessy (Q9XYV9), a putative Hox gene target [The likely e target , indicatY6B3B.10 is most probably orthologous to the human gene P27544 and they both seem to belong to the PF03798 (LAG1) domain family. LAG1 is a longevity gene that was cloned from yeast . MembersDrosophila). Neither of these genes has any putative function assigned to them and they have no matching Pfam-A domains. D2013.10 is expressed in several tissues in C. elegans and it exhibits a wildtype RNAi phenotype.D2013.10 is orthologous to Q15055 (human), Q8K1A5 (mouse) and Q9VX39  regions predicted for each protein, is similar to the number predicted for each cluster of putative TM proteins from the former study (± 1 TM region) . For therrf-3 (RNAi sensitive), respectively. This is in agreement with previous experiments, where 10.3% (N2) and 12.8% (rrf-3) phenotypes have been detected [sft-4 are consistent with previous results [rrf-3 [We observed an RNAi phenotype for 11.8% (2 of 17) of the genes when using both strains N2 (wildtype) and detected ,44. The  results ,31. Howes [rrf-3  and the When analyzing expression patterns using transcriptional reporter fusions, one issue of concern is whether the pattern observed is the expression pattern of the native gene or not. Ectopic or lack of expression can occur if the putative promoter used does not include all the regulatory elements. Expression in several different cell types in the pharynx and in the posterior intestinal cells of young animals has been attributed to the use of incomplete promoters . AnotherThe putative promoter used in the transcriptional fusion for C36B1.12 is only 1 kb, due to the presence of an upstream gene. Therefore, it is possible that the intestinal expression seen predominantly in young worms and mostly located to posterior intestinal nuclei, is an artifact of the use of an incomplete promoter region . If thisThe transcriptional fusion for T28F3.3 also showed a similar intestinal expression, apart from the specific expression in neurons in the head, ventral nerve cord, vulva and a weak expression in hypodermis. The putative promoter region used is only 0.8 kb, due to the presence of an upstream gene. Thus, the intestinal expression seen for T28F3.3 may once again be related to the use of an incomplete promoter region .gfp fusion from the inheritable extrachromosomal array [Two of the transcriptional fusions (for the genes F14F3.3 and ZK858.6) showed no expression of the reporter gene. This is unlikely due to the use of an incomplete promoter region, since the upstream region included was 2.9 kb and 3.0 kb, respectively. Instead, it might be caused by conditional gene expression, germline silencing or absence of the promoter::al array . For thral array .C. elegans genome that are believed to be in operons (15%). Whether C. elegans operons contain genes of related function or not is still unknown. There are, however, some indications that genes encoding proteins of fundamental biological importance might be clustered into operons. For example, genes for mitochondrial proteins have a strong tendency to be together in operons and also genes encoding splicing proteins [Out of the seventeen genes in this study, three are predicted to be in operons (18%). This is equivalent to the number of genes in the proteins .C. elegans is an excellent model organism for pursuing the functional characterization of these genes, considering its well mapped and relatively sophisticated nervous system. Investigating the function of orthologous proteins using a simple multicellular organism is a suitable approach for the possibility of learning more about the function of a gene not only in one species but also hopefully in several. This approach becomes even more valid as several genomes are being sequenced at the moment with additional ones already in the pipeline. With the enormous amount of data that these sequencing efforts are generating, it is very useful to be able to start delineating the gene function based on functional characterization of the ortholog in another species, before initiating studies in more complex organisms.This study has shed some light upon the putative function of a few predicted worm-human orthologs. Our aim was to identify genes that could play a role in the nervous system and indeed we have been able to find eight genes that appear to be expressed in neurons. The membrane topology was predicted with nine different methods, and the SFINX tool ,9, was uThe Pfamseq database version 10.0  was usedThe Pfamseq database  was searPfam-A domains were assigned using the Pfam database ,12. PfamC. elegans strains were as previously described [rrf-3(pk1426) II) [rrf-3 mutant strain has an increased sensitivity to RNAi, also for neuronal genes [dpy-5(e907) I) was used for generating the transgenic lines [Maintenance and handling of escribed . Strains426) II)  (Caenorh426) II) ). The rral genes , which oic lines .C. elegans using TRIzol (Invitrogen Life Technologies, Carlsbad, CA) was reverse transcribed using Reverse Transcription System (Promega, Madison, WI) and then PCR products were generated using Advantage™ 2 PCR Enzyme System  with gene specific primers as well as primers for spliced leader 1 (SL1) and SL2 (Invitrogen Life Technologies): 95°C 60 s, 35 cycles of (95°C 30 s, 55°C 30 s, 68°C 4 min) followed by an additional extension at 68°C 4 min. See E. coli bacterial strain (Promega). Colonies were screened using XmaI, correct colonies were grown in overnight cultures and DNA was extracted using QIAfilter Plasmid Kit (QIAGEN, Hilden, Germany). The vector with the insert was sequenced using ABI PRISM® Big Dye™ Terminator Cycle Sequencing Ready Reaction Kits (Applied Biosystems, Foster City, CA).Total RNA extracted and purified from rrf-3 were used for RNAi screening by feeding [E. coli HT115(DE3) was transformed with the L4440 vector  was used (Fire Laboratory vector pPD34.09 [Strains N2 and  feeding ,65. CGC  feeding  bacteriaboratory ) containroodsize ,44. The pPD34.09 ). Empty The L4440 vector (Fire Laboratory ) containgfp (green fluorescent protein) and unc-54 3'UTR amplified from vector pPD95.67 (Fire Laboratory [dpy-5(+) plasmid (pCeh361) in 1xTE buffer to generate an extrachromosomal array. Analysis of the expression patterns of the different transgenic lines was performed at the Vaz Gomes Laboratory, Karolinska Institutet, Sweden.The transgenic lines were constructed at the Baille Laboratory, Simon Fraser University, Canada . Transcrboratory ). See adAH carried out the membrane topology predictions, phylogenetic analysis, domain assignments, RNAi studies and analysis of gene expression in the transgenic worm strains. ES participated in the membrane topology predictions, phylogenetic analysis and domain assignments. DB contributed in making the transgenic worm strains. AVG participated in the RNAi studies and analysis of gene expression. All authors read and approved the final manuscript.Primer sequences used in RNAi and gene expression studies.Click here for file


Two major questions were addressed in this paper. Firstly, the cause of DNA periodicity, which was investigated by comparisons between real and simulated coding sequences. Secondly, quantification of DNA periodicity was made using an evolutionary algorithm, which was not previously used for such purposes.The periodic pattern of DNA in exons is a known phenomenon. It was suggested that one of the initial causes of periodicity could be the universal  at each location along phase 0 exons were calculated for C. elegans, D. melanogaster and H. sapiens. Two models were used to fit these data, with the key objective of describing periodicity. Both of the models showed that the best-fit curves closely matched the actual data points. The first dynamic period determination model consistently generated a value, which was very close to the period equal to 3 nucleotides. The second fixed period model, as expected, kept the period exactly equal to 3 and did not detract from its goodness of fit.Frequencies of the nucleotides (and the dinucleotide Conclusion can be drawn that DNA periodicity in exons is determined by codon usage frequencies. It is essential to differentiate between DNA periodicity itself, and the length of the period equal to 3. Periodicity itself is a result of certain combinations of codons with different frequencies typical for a species. The length of period equal to 3, instead, is caused by the triplet nature of genetic code. The models and evolutionary algorithm used for characterising DNA periodicity are proven to be an effective tool for describing the periodicity pattern in a species, when a number of exons in the same phase are analysed. Periodicity of DNA in exons, with the period being equal to 3 nucleotides, has been well known for some time -6. This nRNY)pattern (R = A or G, Y = C or U, N = any base), which probably was inherited from the earliest mRNA sequences [The triplet code has undergone evolution itself, from the earliest form of the triplet code to what exists today. The universal DNA periodicity observed in exons suggests a  of real species as the only source of information. The computer program GENERATE, which was used in these experiments, composed artificial coding sequences using CUF of several species as the only source of information. Thus despite random choice the frequencies of codons in simulated sequences were very similar to the real CUF.Adenine nucleotides in real Drosophila melanogaster exons (phase 0) and in simulated . In the simulated coding sequences periodicity is also highly pronounced and the periodicity patterns observed in D. melanogaster exons and simulated sequences are nearly identical . Periodicity of other nucleotides was also observed and it shown high similarity in both DNA of real exons and simulated sequences (data are not shown). The obvious conclusion following from this study is that CUF, which was the only source of information for the simulated coding sequences, is the crucial factor determining periodicity.As an example of these experiments, Figure d Figure  coding sl Figure  &1B. OthAdenine and Thymine (data are not shown) was caused by the fact that 3 stop codons and the corresponding combinations of nucleotides were present in the simulated coding sequences in different and much lesser frequencies than other codons. Cytosine, which is not a component of any stop-codon, does not show periodic pattern at all because frequencies of Cytosine containing codons were equal to frequencies of all other non stop codons. Finally, when frequencies of all codons, including stop codons, were made equal, no periodicity was observed in the simulated sequences  with moolution, .AG was run through the analysis for both dynamic period determination best-fit curve, and the static period of 3 best-fit curve. This was done for the three studied species, C. elegans, D. melanogaster and H. sapiens for phase 0 exons. Several other dinucleotide pairs also shown clear periodicity patterns in exons (data are not shown). AG was used in this paper as an example. Introns were only run through the analysis for dynamic period determination and did not show clear and stable periodicity.To test if the above method could identify a quantifiable period in exons, several tests were performed. All phase 0 exons were extracted from the EID database, described in Material and Methods. This procedure dramatically enhanced the visible periodicity compared to exons of all three phases Figure .. Once tAG for C. elegans, D. melanogaster and H. sapiens phase 0 exons. These curves were created using model 1, in an attempt to find a periodicity within the given data. The first few positions of exons are frequently under different selection pressures, which do not always conform to the same pressures as the remainder of the exon. It was for this reason that the algorithm was run starting from position 20 to position 100 in exons.Curves of best-fit were created for the four separate nucleotides and the dinucleotide C. elegans, D. melanogaster and H. sapiens as they were determined by the analysis. The amplitude of the periodicity was measured as the variation from the center-point of the sine curve. As mentioned earlier, the criterion value of goodness of fit is the sum of squared deviations between observed frequencies and frequencies predicted by the model with the prevailing parameters. This means that as the analysis runs through its generation cycles, it finds better fitting curves as it goes along and replaces the previous curve of best-fit. The criterion value is a reflection of this process, in that as it gets closer to zero, the closer the curve of best-fit represents actual data. The data show that in all cases, the determined period is very close to 3 in exons for all three species for all nucleotides and the dinucleotide pair AG. The range of periods goes from a low of 2.990391 in C. elegans nucleotide C, a difference from 3 is ~0.0096, to a maximum of 3.019349 in C. elegans dinucleotide pair AG, a difference from three of ~0.0193. The criterion values of goodness of fit are also very low for exons, with the largest among them at 0.0093573 in H. sapiens nucleotide C.Table A and actual frequencies of nucleotide A in exons of the three studied species can be seen in Figure AG. Best-fit curves for nucleotides C, G and T are not shown. The blue points on the graph represent data points predicted by fitting model 1 to actual data, represented by pink points. As can be seen in the graphs, the blue best-fit curves in both Figure A comparison between the best-fit curves for nucleotide Adenine in C. elegans D. melanogaster and H. sapiens phase 0 exons with the period fixed at three. Figure AG in 0 exons in the same species. In both sets of graphs, pink points represent actual frequencies of nucleotides and the dinucleotide pair, AG, while the blue points represent the optimized curve of best-fit for these frequencies. It is clear from the graph that keeping the period fixed at exactly three in exons does not detract from the accuracy of the curve of best-fit. The curve of best-fit is remarkably similar to the actual data points.The data were then fitted to model 2, keeping the period fixed at 3. With this algorithm, the ideal best-fit curve would remain nearly in the same pattern. Again phase 0 exons were used as sample data. Figure The fact of DNA periodicity in exons, as well as the lack of periodicity in introns, is known for some time -6. "SuchC. elegans than for the two other species under consideration.The results presented in this paper also demonstrate effectiveness of the evolutionary algorithm and the both models used to identify a periodicity pattern in exons. Although periods, which are seen in Table Introns do not show any specific period that can be determined by the analysis (results are not shown). Although the analysis does produce a period for each data set given, these periods are not consistent with each other, and the predictions do not fit the data well. As introns are not composed from codons, this is an additional indication supporting the conclusion that CUF determine periodicity pattern in exons.Since only exons show a strong periodicity of three, this type of analysis can be in principle used as an additional component of exon finding tools. Such possibility was already considered . UnfortuConclusion can be drawn that DNA periodicity in exons is determined by codon usage frequencies. It is essential to differentiate between DNA periodicity itself, and the length of the period equal to 3. Periodicity itself is a result of certain combinations of codons with different frequencies typical for a species. The length of period equal to 3, instead, is caused by the triplet nature of genetic code. The models and evolutionary algorithm used for characterising DNA periodicity are proven to be an effective tool for describing the periodicity pattern in a species, when a number of exons in the same phase are analysed.C. elegans, D. melanogaster and H. sapiens, was extracted from the exon-intron database (EID), which was compiled in the W. Gilbert laboratory, Department of Molecular and Cellular Biology, Harvard University [C. elegans 14,836 genes and 98581 exons; D. melanogaster 13,361 genes and 58,801 exons, H. sapiens 7150 genes and 47908 exons.Information relevant to iversity . The datThis program calculates frequencies of nucleotides or any combination of nucleotides in a database. exScan align all exons in the database at either the 5' or 3' end. The program then searches the exons for given sequences and give a summary of the sequences found. exScan was used in order to obtain the frequencies for nucleotides and dinucleotide pairs in each position of exons aligned at the 5' end. A summary of the program's operation follows:- Command line for exScan selects the database to be used for searching.- The string(s) to be searched are also entered into the command line.- ExScan then aligns the exons by the 5' end and searches each exon for all matches to the search strings.- The output of the program will provide the number of matches for each search string entered at every position along the aligned exons.- These numbers are then converted into frequencies.ExScan is written in the C++ programming language; its full description and the program itself are available upon requestThe program simulates a required number of coding sequences, using as an input file CUF for a particular species and a generator of random numbers. Inclusion of stop-codon in a coding sequence terminates the gene. There are options, which allow establishing a minimal and a maximal length of genes as well a shape of gene length distribution. GENERATE was used in this study to show how CUF alone could create periodicity, even in randomly created sequences, which do not code for any real protein. The procedure when running GENERATE follows:. Thus, despite random choice the frequencies of codons in simulated sequences were very similar to the real CUF.- GENERATE accepts as input a file containing usage frequencies of all 64 codons. These codon usage frequencies for different species were taken from the database located at - These frequencies are then used to construct a requested number of artificial genes, with the codons chosen randomly based on their frequencies.ATG, and will terminate once a stop codon is randomly chosen.- Artificial genes all start with - The artificial genes can then be used as a separate database for analysis with exScan as above.GENERATE was written in the C++ programming language. Description of the program and program itself are available upon request.The specific method used for fitting the two periodicity models was Differential Evolution (DE). As DE is a widely applicable method of general utility for optimization, the reader is directed to Storn and Price  for detab parameters and is characterized by its fitness .- A population of candidate solutions is established. Each population member is constituted by a randomly sampled set of generation. A challenger is constructed as follows:- For each population member, a challenger is constructed. If this challenger has superior fitness, it will replace the population member in the next a, b and c. Each parameter is then addressed in turn. With probability CR (CR = 0.4 was adopted) the parameter is simply taken from the population member that the challenger is challenging. Otherwise, a new parameter value is constructed as the value for member a plus F times the difference of the values for b and c. For this application, F = 0.4, except F = 1 every fourth generation and F = 2 every seventh generation, to help avoid local optima. In addition, mutation independent from differences between other solutions was invoked periodically, also to help avoid local optima.- Three other population members are chosen at random. We can label these as - Successful challengers replace their respective population members, and, together with surviving members constitute a new generation with higher mean fitness. The process continues over sufficient generations to achieve convergence close to an optimal solution, with the most fit solution being chosen.None declared.SE conducted the data analysis and was involved in drafting the manuscript. FE designed and wrote the code for the C++ computer programs. BK assisted with the manuscript and wrote the code for modeling and fitting periodicity. AR drafted the manuscript, performed statistical analysis, conceived the study and participated in its design and coordination. All authors read and approved the final manuscript.


To identify the spectrum of malignant attributes maintained outside the host environment, we have compared global gene expression in primary breast tumors and matched short-term epithelial cultures.TGFβ signal transduction pathway, such as SPARC, LOXL1, RUNX1, and DAPK1. Underlying this profile was the conspicuous absence of hTERT expression and telomerase activity, a significant increase in TβRII, its cognate ligand, and the CDK inhibitor, p21CIP1/WAF1. Concurrently, tumor tissue and primary cultures displayed low transcript levels of proliferation-related genes, such as, TOP2A, ANKT, RAD51, UBE2C, CENPA, RRM2, and PLK.In contrast to immortal cell lines, a characteristic 'limited proliferation' phenotype was observed, which included over expressed genes associated with the Our data demonstrate that commonly used immortal cell lines do not reflect some aspects of tumor biology as closely as primary tumor cell cultures. The gene expression profile of malignant tissue, which is uniquely retained by cells cultured on solid substrates, could facilitate the development and testing of novel molecular targets for breast cancer. The vast majority of such cultures (>90%) display a finite mitotic lifespan. While molecular changes underlying growth cessation in non-malignant breast epithelial cultures are well studied [reviewed in [In breast cancer, cell based experiments are largely conducted with a few spontaneously arising cell lines from late stages of disease that demonstrate unlimited proliferation . However, the breadth of tumor heterogeneity and early stages of breast tumorigenesis remain under represented in such assays. We, and others have adapted tissue processing and culture conditions to enable the selective isolation and expansion of tumor cell populations from primary breast cancer -9. Closeiewed in ], barriein vitro. In depth comparative analysis of such isogenic malignant cell populations could provide important insights regarding cellular pathways that function independently of environmental constraints, as well as the genome wide consequences of constitutive growth arrest of breast tumor cells widely encountered in laboratory dishes. Here we describe a comprehensive comparison of matched samples of primary breast carcinoma tissue, and early passage tumor cultures. We have used cDNA microarray-based global gene expression profiling to determine common molecular phenotypes of tumor tissue and tumor-derived epithelial cultures, which distinguish them from cancerous and non-cancerous immortal cell lines.In contradistinction to previous reports on the characteristics of rare immortal cell lines developed from primary tumors ,5,7,8,12In order to ensure the propagation of tumor epithelium without apparent contamination with fibroblasts and other cellular components, previously described methods including the selective release of nests of malignant cells from the connective tissue, were used ,3. As ilSeventeen RNA samples were analyzed by cDNA microarrays. These were comprised of 4 cases of primary breast tumor tissue and 1–2 matched tumor cultures, 2 additional unmatched tumor cultures, and 4 immortal breast cell lines. Cell lines were selected to represent estrogen receptor (ER) positive (T47D) and ER negative (SKBR3, BT20) tumors, and non-cancerous breast epithelium (ENUt7, ref. ).Unsupervised clustering analysis of 7362 clones (4743 unique genes/ESTs) grouped the samples into three separate clusters representing immortal cell lines, tumor tissue, and tumor cultures. Figure RFC4, CENPA, TOP2A, CCNA, MCM7, PCNA, CDC2) were primarily those associated with the 'proliferation' cluster described by Ross et al [MAL, MAFB, RUNX1, KRT5), in the induction of apoptosis (DAPK1), and in tumor angiogenesis, and extravasation (SPARC)  for independently verifying genes that are differentially expressed between classes or groups of samples. SAM analysis identified 930 clones, representing 681 unique genes/ESTs, whose expression was significantly (>2-fold) different between group 1 comprised of immortal cell lines, and group 2 comprised of tumor tissue and tumor cultures . A full list of the differentially expressed genes is provided in [Table S2 – see http://genome-www.stanford.edu/breast_cancer/PTCC/]. As expected on the basis of the similarity in gene expression observed between tumor tissue and tumor cultures in the unsupervised array data Figure , these css et al . In contons, ref ).Primary epithelial cell cultures, in contrast to fibroblasts and rapidly growing cell lines, undergo rapid growth arrest in 10% fetal calf serum (FCS). This is why we, and others have propagated cultures of primary tumor epithelium in 0–5% FCS (1–9). Immortal cell lines, however, grow optimally in 10% FCS; lower concentrations retard growth. Therefore, to optimize growth conditions for both, 2% FCS was chosen for primary tumor cultures and 10% FCS for cell lines. It is conceivable that an increased concentration of FCS may account for differences in gene expression between primary tumor cultures and immortalized cell lines. In this case, one would expect increased proliferation in the primary tumor cultures at 10% FCS, when in fact, growth is severely inhibited by this approach.As summarized in Table hTERC and hTERT subunits of telomerase, encoding the structural RNA component, and the component with reverse transcriptase activity, respectively, was measured. While commonly used immortal cell lines (T47D, MDA231) displayed several-fold higher transcript levels for hTERT, undetectable to minimal levels were observed in 6/8 independent primary tumor cultures (1599T, 713T, 1569T, 1570T, 1617T, 1620T). The primary tumor culture with the highest relative expression of hTERT (257T) has developed into an immortal cell line , or enzymatic digestion (DIG) of the tumor sample 054T  members of the INK family of CDKIs, p15INK4B, and p16INK4A (3) members of the TGF-β family, TGFβI, TGFβII and the signaling receptors, TβRI, and TβRII. As illustrated in Figure p21CIP1/WAF1 levels were 2 to138 fold lower than steady state levels in normal breast epithelium. In contrast, 4/20 primary tumor culture samples showed this range of p21CIP1/WAF1 expression (p = 0.0003). For the CDKIs, p27KIP1, and p57KIP2, several fold decrease in gene expression was observed in all primary tumor cultures and most immortal cell lines as well (p = 0.04 and 0.69, respectively). Similarly, no significant differences were apparent for p15INK4B, and p16INK4A gene expression in the two groups (p = 0.07 and 0.39, respectively). For members of the TGF-β family, while significant differences were not observed in the expression of TGFβI and TβRI, a median increase of 2 fold and 4 fold were found in the expression of TGFβII and TβRII respectvely. In contrast, immortal cell lines, showed a median decrease of 7-fold for TGFβII and 4-fold for TβRII (p = 0.0035 and 0.0011, respectively). All p values were derived by the Mann-Whitney test.In the next step towards identifying the determinants of replicative arrest, primary tumor cultures were compared with immortal cell lines for relative expression of genes associated with the negative regulation of the cell cycle in general, and with epithelial cell proliferation in particular. This analysis included 9 candidate genes in 3 signaling pathways: (1) members of the CIP/KIP family of cyclin-dependent kinase inhibitors (CDKIs), We used MANOVA to compare expression of the 9 above-mentioned genes in immortal cell lines, primary tumor cultures, and normal breast epithelium. It was apparent that the multivariate means for the 9 genes differed for each of the 3 groups above and that both immortal lines and primary tumor cultures differed from normal breast epithelium (p = 0.0001) as also depicted in the hierarchical clustering dendrogram of these samples Figure . A 3-D pPCNA, CKS1B, TPX2, UBE2C, CDC6, confirmed by the statistically significant analysis of microarray data, SAM, were among the positive proliferation genes differentially expressed by immortal cell lines, and matched tumor tissue/cell culture samples  were confirmed by QRT-PCR  as was the contrasting gene 'signature' in cultures derived from independent cases of breast carcinoma. Together, these observations have led us to conclude that the continuous selection of rapidly proliferating cells culminating in the immortalized phenotype favors the loss of several aspects of gene expression retained by early passage primary tumor cultures.TGFβ resulting in a significant increase in transcription of the CDKI, p21CIP1/WAF1, consistent with the findings of induction or stabilization of this gene in a variety of immortalized cells exposed to exogenous TGFβ [p21CIP1/WAF1 in turn appear to have a direct impact on telomerase regulation [in vivo, p21CIP1/WAF1 positive cells are detected in the majority of cases [TGFβ transcription [TGFβ gene contains a shear stress response element [in vitro environment and initiates a cascade of growth inhibitory events, including telomerase inactivation. While epithelial cells derived from most primary breast tumors rapidly revert to regulated growth dictated by environmental signals, immortal breast epithelial cells are insensitive to such cues, which may be the underlying basis for their continued selection in vitro. Notably, loss or functional inactivation of the cognate receptor often enables tumor cells in vivo to overcome the growth inhibitory effect of TGFβ early in tumorigenesis, however, during metastatic progression, autocrine TGFβ appears to play a tumor-promoting role, possibly enabling tumor cells to survive significant changes in microenvironment [TGFβ in response to the in vitro environment in primary breast tumor cultures portrays a critical phase of tumor progression. Additionally, towards the full manifestation of differentiated phenotypes in primary tumor cultures, cell propagation in a three-dimensional growth matrix, pioneered by Bissell and colleagues [Our data suggests that the limited growth potential of primary tumor cultures results from two major proliferative barriers, (a) telomerase inactivation, potentially leading to telomere attrition, and (b) negative growth signaling by upregulated ous TGFβ ,17. Elevgulation . As expeof cases . Also pecription . Since t element , it seemironment . In thislleagues , is impeTumor-derived immortal cell lines generally display robust proliferation and have thus filled an important need for functional cancer cell model systems. While immortal cell lines continue to provide molecular and biological insights regarding proliferation-related hallmarks of malignancy, their functional application as indicators of efficacy in cancer drug development is relevant mostly to rapidly proliferating high-grade tumors. Many breast tumors do not fall into this category, and are not necessarily indolent. Thus, additional targets in diverse gene clusters must be identified for novel drug designing. In fact, such targets could be applicable to tumors at early or late stages as recent studies suggest that genes conferring invasive and/or metastatic characteristics late in progression often become dysfunctional at earlier stages of tumorigenesis . Moreovein vitro model systems. Thus the opportunity for basic and clinical application of functional cells derived from the full range of pathological breast tissue, instead of a few immortal cell lines should not be missed.This microarray-based analysis demonstrates that epithelial cultures isolated from primary breast tumors retain phenotypes of the malignant tissue, which are eliminated during the selection of rapidly proliferating cell populations that comprise commonly used ENUt7 cell line were propagated in low calcium MCDB170 medium supplemented with 2% FCS. Breast cancer cell lines were cultured in the recommended growth media supplemented with 10%FCS; MCF7, BT20, MDA231, MDA157 in DME-H21; BT474, CAMA1, T47D, ZR75 in RPMI 1740; MDA435, MDA134, MDA361 in L15.Pathologically confirmed tumor tissue and non malignant reduction mammoplasty samples were collected as fresh specimens under IRB approved guidelines at the California Pacific Medical Center, San Francisco, Stanford University, and the University of California, San Francisco between 1997 – 2000. Additional tumor samples were obtained from the NCI Cooperative Human Tissue Network. A portion of the tissue was snap frozen and cryopreserved for histological confirmation and nucleic acid isolation prior to cell culture. Tumor samples were processed as previously described . A totalTo confirm the epithelial phenotype of primary tumor derived cells, cultures fixed with 50% ethanol: acetone were immunostained with anti pan-cytokeratin, as previously described .® Pro 4.0 software (Axon Instruments). Spots were selected for analysis if signal intensities in both Cy3 and Cy5 channels were 2.5 times higher than background. The arrays were clustered using a hierarchical clustering algorithm [Frozen tissue was trimmed to yield >90% tumor cell enrichment. Total RNA was extracted with the RNAeasy Mini kit (Qiagen), amplified using an optimized T7 based protocol , labeledlgorithm , which glgorithm . The cluSignificance Analysis of Microarrays ,31, usedT). The higher the initial amount of transcript template, the lower the CT value. For quantitation of gene expression, the target gene value normalized to the expression of an endogenous reference (β actin) was designated as ΔCT. Relative gene expression was calculated by the formula 2-ΔΔCT. ΔΔCT was obtained by subtracting the ΔCT of test sample from the ΔCT of normal breast epithelial fractions, called 'organoids', isolated from mechanically and enzymatically dissociated reduction mammoplasty tissue (average of 8 independent specimens).RNA samples were treated with RNAse-free DNAse (Roche) to remove genomic DNA, and reverse transcribed. Fifty ng of cDNA was used as template for PCR amplification with specific oligonucleotide primers, (designed using Primer Express 1.5 software). Following denaturation and cycling reactions (40 cycles) the products were analyzed (Applied Biosystems 5700 Sequence Detection System). The cycle number at which the amount of amplified target reached a fixed threshold was designated as the threshold cycle  as per manufacturer recommendation (Intergen). Briefly, trypsinized cell pellets were resuspended in lysis buffer at 500 cells/microliter, incubated on ice for 30 minutes, centrifuged at 12,000 × g for 20 mins, and the telomerase containing supernatant used for PCR amplification. Reaction products were resolved in 10% PAGE gels and visualized with SYBR Green-1 (Molecular Probes). Heat inactivated controls were included for each sample. Linearity of the assay was determined with cell dilutions ranging from 100 to 10, 000 cells.We calculated Pearson correlations from the microarray data generated from 38,999 clones to measure similarities among 7 matched tumor tissue: cell culture sample pairs and among 16 tumor tissue: immortal cell line pairs. For QRT-PCR data, multivariate analysis of variance (MANOVA) was used to compare the expression levels of genes involved in negative growth regulation between immortal cell lines, primary tumor cultures, and non malignant breast epithelial cells. Tests for statistical significance were based on Wilks lamda criterion, a multivariate analog of the F-test for univariate analysis of variance (ANOVA), which tests the equality of means. Calculations were made in Data Desk, version 6.2. Principal components for all genes over all the data were computed and S-Plus was used to plot the data for the first 3 principal components.SHD conceived the study, provided overall direction and coordination to the research, and drafted the manuscript. YJ carried out the microarray analysis. YB carried out QRT-PCR, and other molecular analyses. DHM performed the statistical analysis of data. ZM performed the pathology review and tissue dissection. SSJ participated in the design and analysis of microarray-based experiments and in editing portions of the manuscript. All authors read and approved the final manuscript.


The timescale of prokaryote evolution has been difficult to reconstruct because of a limited fossil record and complexities associated with molecular clocks and deep divergences. However, the relatively large number of genome sequences currently available has provided a better opportunity to control for potential biases such as horizontal gene transfer and rate differences among lineages. We assembled a data set of sequences from 32 proteins (~7600 amino acids) common to 72 species and estimated phylogenetic relationships and divergence times with a local clock method.Deinococcus, and Cyanobacteria) that we call Terrabacteria and associate with an early colonization of land. Divergence time estimates for the major groups of eubacteria are between 2.5–3.2 billion years ago (Ga) while those for archaebacteria are mostly between 3.1–4.1 Ga. The time estimates suggest a Hadean origin of life (prior to 4.1 Ga), an early origin of methanogenesis (3.8–4.1 Ga), an origin of anaerobic methanotrophy after 3.1 Ga, an origin of phototrophy prior to 3.2 Ga, an early colonization of land 2.8–3.1 Ga, and an origin of aerobic methanotrophy 2.5–2.8 Ga.Our phylogenetic results support most of the currently recognized higher-level groupings of prokaryotes. Of particular interest is a well-supported group of three major lineages of eubacteria (Actinobacteria, Our early time estimates for methanogenesis support the consideration of methane, in addition to carbon dioxide, as a greenhouse gas responsible for the early warming of the Earths' surface. Our divergence times for the origin of anaerobic methanotrophy are compatible with highly depleted carbon isotopic values found in rocks dated 2.8–2.6 Ga. An early origin of phototrophy is consistent with the earliest bacterial mats and structures identified as stromatolites, but a 2.6 Ga origin of cyanobacteria suggests that those Archean structures, if biologically produced, were made by anoxygenic photosynthesizers. The resistance to desiccation of Terrabacteria and their elaboration of photoprotective compounds suggests that the common ancestor of this group inhabited land. If true, then oxygenic photosynthesis may owe its origin to terrestrial adaptations. The evolutionary history of prokaryotes includes both horizontal and vertical inheritance of genes -3. HorizTemporal information concerning prokaryote evolution has come from diverse sources. For eukaryotes, the fossil record provides an abundant source of such data, but this has not been true for prokaryotes, which are difficult to identify as fossils ,21. LimiIn contrast to conventional interpretations of cyanobacteria as being among the most ancient of life forms on Earth , these sIn this study we have assembled a data set of amino acid sequences from 32 proteins common to 72 species of prokaryotes and eukaryotes and estimated phylogenetic relationships and divergence times with a local clock method. These results in turn have been used to investigate the origin of metabolic pathways of importance in evolution of the biosphere.The majority (81%) of the 32 proteins that were used are classified in the "information storage and processes" functional category of the COG. The other categories represented are "cellular processes" (10%), "metabolism" (3%), and "information storage and processing" + "metabolism" . Other studies that have analyzed prokaryote genome sequence data for phylogeny have found a similar high proportion of proteins in the "information storage and processes" functional category, presumably because HGT is more difficult with such genes that are vital for the survival of the cell ,33,34.The concatenated and aligned data set of 32 proteins contained 27,205 amino acid sites (including insertions and deletions). With alignment gaps removed, the two data sets analyzed were 7,338 amino acid sites (Archaebacteria) and 7,597 amino acid sites (Eubacteria). The data sets were complete in the sense that sequences of all taxa were present for all proteins.Buchnera among γ-proteobacteria [Deinococcus  and the hyperthermophiles (Thermotoga, Aquifex) branch basally in the tree. These groups and relationships are similar to those found previously with analyses of prokaryote genome sequences [The phylogeny of eubacteria Fig.  shows sibacteria ,36. Its equences ,33,34.Methanopyrus kandleri, has differed among previous studies [The phylogeny of archaebacteria Fig.  agrees w studies -49. HoweTimes of divergence were estimated for all nodes in the phylogenies of eubacteria Fig.  and archThe times estimated with the fossil calibration point in the archaebacteria data set were on average only 10% younger than the ones estimated with the molecular calibration. Moreover there was even a smaller effect on the time estimates of the deepest nodes, which were the ones of interest in this study (node M 3.2%, node N 2.1%, node O 1.8% and node P 1.3%). This variation is due not only to the different calibration times but also to the type of constraints used (i.e. minimum boundaries only vs. minimum and maximum bounds).A single timetree Fig.  was consEscherichia coli from Salmonella typhimurium  and heterocyst-forming (Nostoc sp.) cyanobacteria. Our time estimate for this divergence is 0.70–1.41 Ga . Therefore, methanogenesis presumably arose between nodes P and O, or between 4.11 Ga (3.31–4.49 Ga) and 3.78 Ga (3.05–4.16 Ga) , node N , suggesting even earlier dates for the last common ancestor of living organisms and the origin of life. This is in agreement with previous molecular clock analyses using mostly different data sets and methodology ,30. A HaThe lower luminosity of the sun during the Hadean and Archean predicts that surface water would have been frozen during that time. Instead there is evidence of liquid water and moderate to high surface temperatures ,61. The Archaebacteria are the only prokaryotes known to produce methane. Our time estimate of between 4.11 Ga (3.31–4.49 Ga) and 3.78 Ga (3.05–4.16 Ga) for the origin of methanogenesis suggests that methanogens were present on Earth during the Archean, consistent with the methane greenhouse theory . NonetheArchaeoglobus fulgidus. The particular condition of this archaebacterium has been explained with an ancient horizontal gene transfer from an eubacterial lineage, most likely a δ-proteobacterium [Anaerobic methanotrophy, or anaerobic oxidation of methane (AOM), is a metabolism associated with anoxic marine sediments rich in methane. This metabolism is characterized by the coupling of two reactions, oxidation of methane and sulfate reduction. The methane oxidizers are represented by archaebacteria phylogenetically related to the Methanosarcinales, while the sulfate reducers, when present, are eubacterial members of the δ-proteobacteria division . These tacterium ,72.The phylogenetic position of the anaerobic methanotrophs with the Methanosarcinales places the maximum date for the origin of this metabolism at 3.09 2.47–3.51) Ga  and node B   is restricted to eubacteria among prokaryotes. Phototrophic eubacteria are found in five major phyla (groups), including proteobacteria, green sulfur bacteria, green filamentous bacteria, gram positive heliobacteria, and cyanobacteria ,76. OnlyThere are three explanations for this broad taxonomic distribution of phototrophic metabolism; it evolved in one lineage of eubacteria and spread at a later time to other lineages by horizontal transfer, the common ancestor of these groups possessed this metabolism and genetic machinery, or there was a combination of horizontal transfer and vertical inheritance . BecauseAquifex and Thermotoga are not phototrophic and branch more basally, 3.64 (3.17–4.13) Ga (Node J) can be considered a maximum date for phototrophy. However, if those hyperthermophiles instead occupy a more derived position on the tree, as some analyses have indicated [Therefore, we estimate that phototrophy evolved prior to 3.19 (2.80–3.63) Ga  also share a high resistance to dehydration [terra, land or earth) for the group that includes the bacterial phyla Actinobacteria, Cyanobacteria, and Deinococcus-Thermus. An early colonization of land is inferred to have occurred after the divergence of this terrestrial lineage with Firmicutes , such minimum constraints permit the estimated time to be much older in a Bayesian analysis. However, in this case, the time estimated for node E  can be retrieved from genes in the prokaryote genome that have not been affected by horizontal gene transfer. Our prokaryotic timeline shows deep divergences within both the eubacterial and archaebacterial domains indicating a long evolutionary history. The early evolution of life >4.1 Ga) and early origin of several important metabolic pathways  suggests that organisms have influenced the Earth's environment since early in the history of the planet  whose addition to the data set would have resulted in a substantial reduction in the total number of proteins. Data assembly began with the Clusters of Orthologous Groups of Proteins (COG) [We assembled a dataset that maximized the number of taxa and proteins from available organisms with complete genome sequences of prokaryotes and selected eukaryotes. In doing so, we omitted a few taxa (e.g., ns (COG) , which cns (COG) . In totaAeropyrum pernix K1 (NC_000854), Archaeoglobus fulgidus (NC_000917), Methanothermobacter thermoautotrophicus str. Delta H (NC_000916), Methanococcus jannaschii (NC_000909), Methanopyrus kandleri AV19 (NC_003551), Methanosarcina acetivorans str. C2A (NC_003552), Methanosarcina mazei Goe1 (NC_003901), Pyrobaculum aerophilum (NC_003364), Pyrococcus abyssi (NC_000868), Pyrococcus furiosus DSM 3638 (NC_003413), Pyrococcus horikoshii (NC_000961), Sulfolobus solfataricus (NC_002754), Sulfolobus tokodaii (NC_003106), Thermoplasma acidophilum (NC_002578), Thermoplasma volcanium (NC_002689).The species of Archaebacteria and their accession numbers are: Aquifex aeolicus (NC_000918), Bacilllus halodurans (NC_002570), Bacillus subtilis (NC_000964), Borrelia burgodorferi (NC_001318), Brucella melitensis (NC_003317, NC_003318), Buchnera aphidicola str. APS (Acyrthosiphon pisum) (NC_002528), Campylobacter jejuni (NC_002163), Caulobacter crescentus CB15 (NC_002696), Chlamydia muridarum (NC_002620), Chlamydia trachomatis (NC_000117), Chlamydophila pneumoniae CWL029 (NC_000922), Chlorobium tepidum str. TLS (NC_002932), Clostridium acetobutylicum (NC_003030), Clostridium perfringens (NC_003366), Corynebacterium glutamicum ATCC 13032 (NC_003450), Deinococcus radiodurans (NC_001263, NC_001264), Escherichia coli O157:H7 EDL933 (NC_002655), Fusobacterium nucleatum subsp. nucleatum ATCC 25586 (NC_003454), Haemophilus influenzae Rd (NC_000907), Helicobacter pylori 26695 (NC_000915), Lactococcus lactis subsp. lactis (NC_002662), Listeria innocua (NC_003212), Listeria monocytogenes EGD-e (NC_003210), Mesorhizobium loti (NC_002678), Mycobacterium leprae (NC_002677), Mycobacterium tuberculosis H37Rv (NC_000962), Mycoplasma genitalium G-37 (NC_000908), Mycoplasma pneumoniae (NC_000912), Mycoplasma pulmonis (NC_002771), Neisseria meningitidis MC58 (NC_003112), Nostoc sp. PCC7120 (NC_003272), Pasteurella multocida (NC_002663), Pseudomonas aeruginosa PA01 (NC_002516), Ralstonia solanacearum (NC_003295), Rickettsia conorii (NC_003103), Rickettsia prowazekii (NC_000963), Salmonella enterica subsp. enterica serovar Typhi (NC_003198), Salmonella typhimurium LT2 (NC_003197), Sinorhizobium meliloti (NC_003047), Staphylococcus aureus Mu50 (NC_002758), Streptococcus pneumoniae TIGR4 (NC_003028), Streptococcus pyogenes M1 GAS (NC_002737), Streptomyces coelicolor A3(2) (NC_003888), Synechocystis PCC6803 (NC_000911), Thermoanaerobacter tengcongensis (NC_003869), Thermosynechococcus elongatus BP-1 (NC_004113), Thermotoga maritima (NC_000853), Treponema pallidum subsp. pallidum str. Nichols (NC_000919), Ureaplasma parvum serovar 3 str. ATCC 700970 (NC_002162), Vibrio cholerae O1 biovar eltor str. N16961 (NC_002505, NC_002506), Xanthomonas campestris pv. campestris str. ATCC 33913 (NC_003902), Xanthomonas axonopodis pv. citri str. 306 (NC_003919), Xylella fastidiosa 9a5c (NC_002488), Yersinia pestis (NC_003143).The species of Eubacteria are: Arabidopsis thaliana, Drosophila melanogaster, Homo sapiens. Accession numbers for eukaryote proteins are presented elsewhere [The eukaryotes were lsewhere .This dataset consisted of 60 proteins that were individually analysed as a step in orthology determination. The proteins were aligned with CLUSTALW . Then phM. kandleri in the Bayesian phylogeny. The sequence alignments and other supplementary data are presented elsewhere [The 32 remaining proteins were concatenated for analysis. The α parameters used during the tree building process were estimated with the program PamL (JTT+gamma model) . From thlsewhere .Time estimation was conducted separately within each domain (Archaebacteria and Eubacteria) using reciprocal rooting and several calibration points. All time estimates were calculated with a Bayesian local clock approach  utilizinFor the archaebacterial data set, we included eukaryotes for calibration purposes because reliable calibration points were unavailable among those prokaryotes. In doing so, only proteins in which eukaryotes clustered with archaebacteria were included . An outgFor the eubacterial data set, we used four internal time constraints in separate analyses, all involving the origin of cyanobacteria. The first and most conservative constraint was a fixed origin (minimum and maximum bounds) at 2.3 Ga, which corresponds to the GOE. For the second constraint we used 2.3 Ga as a minimum bound, with no maximum bound. For the third constraint we used a previous molecular time estimate (2.56 Ga) for the divergence of cyanobacteria from closest living relatives among eubacteria, and fixed the minimum (2.04 Ga) and maximum (3.08 Ga) values to the 95% confidence limits of that time estimate . The fouFor each of these calibration points, all five initial settings were applied, resulting in 15 and 20 analyses for the Archaebacteria and Eubacteria (respectively). The effects of the different initial settings on the analyses were found to be minimal. A 44% difference in the priors, in fact, generated a maximum 2.7%  difference in the time estimates  in the archaebacteria and a maximum 3.5%  difference in the eubacteria  see .AF assembled and aligned the dataset and conducted initial analyses. FUB conducted phylogenetic and molecular clock analyses and co-drafted the manuscript. SBH directed the research and co-drafting the manuscript.Complete time estimation analyses. Estimated times for each node and calibration for Eubacteria and Archaebacteria. The node numbers refer to additional files 1 (eubacteria) and 2 (archaebacteria).Click here for fileEubacteria tree. Phylogenetic tree of eubacteria . Node numbers assigned during the time estimation analyses are represented in italics.Click here for fileArchaebacteria tree. Phylogenetic tree of archaebacteria . Node numbers assigned during the time estimation analyses are represented in italics.Click here for filePrior distribution values. Mean of the prior distribution for the rate of molecular evolution of the ingroup root node (rtrate) in Eubacteria and Archaebacteria.Click here for filePercentage difference. Divergence time estimates and percentage difference due to different ingroup root constraints used under each calibration point. Node numbers refer to additional file 2 (eubacteria) and additional file 3 (archaebacteria).Click here for file


Every time a cell divides, it must first duplicate its entire genome. Barring the occasional error, the daughter cells inherit identical copies of the parent cell's genome. With a typical human cell containing almost 9 feet of DNA made of 3 billion base pairs crammed into a nucleus about 5 microns (.0002 inches) in diameter, that's no small feat. To accomplish the job, cells engage specialized teams of protein machines, each performing different tasks during the various stages of DNA replication: initiation, duplication, quality control, and repair. Much of what we know about the molecular mechanisms of DNA replication comes from studies of bacteria. In the bacterial genome, which consists of several million base pairs, replication begins at a single site, spanning about 100 base pairs. The regulation and mechanisms of replication, even in the compact bacterial genome, are so complex that 51 years after Watson and Crick reported that the structure of DNA “immediately suggests” a mechanism for its replication, biologists are still working out the details and regulation of that mechanism.Before duplication, aptly named initiator proteins bind to DNA at replication initiation sites and break the bonds holding the complementary base pairs together, separating the double helix locally into single strands and creating two Y-shaped junctions at either end called replication forks. At each replication fork, a complex of proteins continues the business of unzipping the DNA and using the exposed single strands as templates to generate complementary daughter strands. What controls when and how individual initiation sites are activated in mammalian cells has remained obscure. Is initiation restricted to specific sites? Do specific DNA sequences control initiation events locally? Examining individual molecules of fluorescently labeled replicating DNA, Paolo Norio and Carl Schildkraut report that initiation events are not controlled by individual initiation sites but occur throughout the genome. And the activation of these sites appears to depend on what's happening at the genomic level.Using a novel technique called single molecule analysis of replicated DNA (SMARD), Norio and Schildkraut use the Epstein Barr virus (EBV) in human B cells as a model system for studying DNA replication. During the latent stage of infection, the EBV genome exists as an episome—a circular piece of extrachromosomal DNA. It replicates only once per cell cycle, during the DNA synthesis stage, and uses its host's replication machinery to do so. Using nucleotide analogs that can be detected by immunofluorescence , the researchers can determine the position, direction, and density of the replication forks, and then determine how replication starts, progresses, and terminates.Norio and Schildkraut studied replication using two strains of the EBV virus grown in human B cells, their natural target. Previous studies, which had largely focused on the activity of individual initiation sites, had suggested that different EBV strains vary in how initiation sites are activated and that specific initiation sites or regions likely regulate replication. Looking at larger genomic regions, Norio and Schildkraut found something different: not only do initiation sites occur throughout the genomes, but their activity “differs dramatically” in the two EBV strains and even within a strain. Differences were seen in the order of initiation site activation, in the direction of replication fork movement, and in the speed of duplication in different parts of the genome. While the two largely similar viral genomes do show some genetic differences, the authors dismiss the idea that these local differences could explain the observed variations in replication control. It's more likely, they conclude, that epigenetic modifications (such as changes in chromatin structure) produce the differences in the order and frequency of activation of initiation sites across genomic regions.It seems that initiation events are not restricted to specific genomic areas, and experimentally induced loss of individual initiation sites does not significantly affect EBV genome replication (because other sites take up the slack). This redundancy provides flexibility in determining which sites are activated. Since the EBV genome uses human replication machinery to duplicate its genome, these findings likely apply to DNA replication in mammalian cells as well. The very survival of the cell—and the health of the organism it inhabits—depends upon the faithful replication of the genome. Using processes that operate at the genomic level may afford cells the means to manage an unwieldy genome, and perhaps, more importantly, guarantee their genes safe passage to the next generation.


Using a new systematic approach to generating cDNA clones containing full-length open reading frames, clones representing 70% of genes on human chromosome 22 were obtained. We have developed a systematic approach to generating cDNA clones containing full-length open reading frames (ORFs), exploiting knowledge of gene structure from genomic sequence. Each ORF was amplified by PCR from a pool of primary cDNAs, cloned and confirmed by sequencing. We obtained clones representing 70% of genes on human chromosome 22, whereas searching available cDNA clone collections found at best 48% from a single collection and 60% for all collections combined. Many methods for high-throughput, experimental elucidation of gene function  depend on the availability of full-length cDNA clone collections . These cExtension of functional genomic approaches to mammalian genomes requires development of adequate ORF clone collections. Several projects based on complete sequencing of clones isolated from cDNA libraries are in place to generate these collections for mouse  and humaThere is still uncertainty over the exact number of human and mouse genes and hence over the completeness of the existing cDNA collections. Therefore, we have investigated a defined subset of genes, namely the full-length protein-coding genes defined in our current annotation of human chromosome 22 . In thisTo extend the coverage of full-length ORF clones, we have developed an alternative method which exploits knowledge of gene structure based on genomic sequence. It involves the specific amplification of a targeted ORF plus short regions of the 5' and 3' untranslated regions from a mixed pool of cDNAs. Amplified fragments are cloned into a standard plasmid sequencing vector and their identity and integrity confirmed by DNA sequencing. The aim of the method is to provide cDNA clones containing confirmed full-length ORFs, which can later be manipulated into suitable vector systems such as Gateway (Invitrogen) or Creator (BD Biosciences) for functional genomics. We have applied this method to the same set of chromosome 22 protein-coding genes and have shown that we can obtain clones representing 70% of the targeted genes with a limited range of experimental conditions. We have also demonstrated a reasonable expectation that we can isolate clones for 83%.We have previously described a gene annotation of chromosome 22  and its p < 0.0007), that is, there is bias against longer ORF clones.We first considered the completeness of available full-length human cDNA collections, by comparing the DNA sequences of available cDNA library clones with our targeted set of 398 ORFs. For this analysis we used cDNA sequences downloaded from the major collections in January 2004. The publicly available cDNA collections analyzed were those from the Mammalian Gene Collection (MGC) , the fulIn summary, there is currently a 60% chance of obtaining a full-length cDNA clone from one of these collections, based on a sample of 1% of the human genome. The best single collection (MGC) provides 48% of the clones. This analysis of coverage, based on the subset of full-length protein-coding genes on chromosome 22, mimics the situation occurring in a positional cloning type strategy where one might want to obtain clones for a region identified by genetic mapping. However, it does not assess whether the collections are enriched or depleted for specific classes of gene by function, tissue distribution or level of expression. As chromosome 22 is particularly GC-rich, and compared to other human chromosomes the set of genes we have used for this assessment may be biased towards housekeeping genes with widespread or ubiquitous expression which are known to be enriched in GC-rich regions of the genome. Hence, results for specific classes of genes will differ. In any case, one can expect to obtain roughly half of the clones required from one of these collections. This is testimony to the considerable effort that has gone into constructing the resources, but is also frustrating, because other sources are required to make up the substantial remainder. To investigate whether other approaches could be used to address the completeness of cDNA clone resources, we developed an alternative method which is complimentary to cDNA library sequencing, and tested this approach on the same set of chromosome 22 ORFs.Caenorhabditis elegans, an alternative strategy has been developed that is directly tailored to clone ORFs defined by gene annotations from cDNA libraries into Gateway vectors ready for functional genomics [Previous efforts in human to obtain cDNA clones suitable for future functional genomics studies have started by isolating the longest possible cDNA clones -13. In Cgenomics . The strPfu-turbo DNA polymerase as an alternative enzyme for one or both rounds of PCR, or using a cDNA template from a single tissue rather than the pooled cDNA. Fragments of the correct size were cloned into a T-tailed plasmid and the inserts were verified by complete sequencing using vector primers and anticipated gene specific primers. Assembled sequence for each clone was then compared with the expected gene sequence. Clones were accepted as correct versions of the ORF if identical to the expected sequence or if they contained only base changes that were known to be single-nucleotide polymorphisms (SNPs) or resulted in silent codon changes. Clones were also accepted with an alternative splicing event that maintained the ORF. Clones were rejected (for this study) if they contained a nonsynonymous base change that could not be confirmed as a known SNP ('unconfirmed bases') or if they resulted from an alternative splice or partially processed mRNA that did not maintain the ORF. When a clone generated from a fragment of the correct size failed validation because of the presence of unconfirmed bases, or retention of a small intron, an alternative clone was picked and sequenced until a correct version was obtained. If alternative splicing or partial processing events gave unacceptable clones, a further round of reamplification was undertaken in order to obtain a correct fragment. Finally, if clone inserts were repeatedly unacceptable as a result of mispriming events, annotation error or amplification of a related gene, a new set of nested oligonucleotide primers were designed.For the 398 targets, a nested set of two pairs of PCR oligonucleotide primers surrounding each ORF and including a short region of the 5' and 3' untranslated regions was identified. As these primers were to be used to extract a fragment containing the ORF from an extremely complex cDNA template, design was not restricted to the sequences at the start and stop of the ORF. A highly processive, proof-reading thermostable DNA polymerase was use to amplify the ORF from a pool of cDNA derived from various tissues using two rounds of PCR. In 76% of cases amplification with KOD Hot Start polymerase was successful in generating a PCR product of expected size under one set of amplification conditions . However, where the expected-sized PCR fragment was not obtained, we were often able to obtain a fragment by subsequent repeat of the procedure with slight modifications including increasing the annealing temperature, using One possible concern with a strategy that involves reverse transcription and multiple rounds of PCR amplification followed by cloning of a single molecule is that the process will introduce base errors that alter the sequence of the final cloned ORF. Analysis of error rate here is complicated by the frequency of SNPs in humans and the fact that the starting cDNA template is a mix of cDNA from multiple human donors. We estimated the error rate from reverse transcription, PCR and the cloning process by sequencing 48 clones (covering 70,656 bases) containing the ORF of the NAGA gene. These were derived by our cloning protocol using cDNA from 10 lymphoblastoid cell lines as a template, as polymorphism would be easier to identify where each cDNA mix could only be one of two haplotypes. We categorized observed base changes as known SNPs if they were found to exist in dbSNP, in ESTs or in independently sequenced cDNA clones. Base changes were categorized as putative errors if no equivalent sequence could be identified. From this analysis we identified six putative base errors, giving an overall estimate of 0.085 errors per kilobase (kb), or one error per 7.8 clones assuming a mean ORF size of 1.5 kb.Applying the strategy outlined above to the 398 chromosome 22 ORFs, we were able to clone and confirm 278 (70%) of the targeted chromosome 22 ORFs . Sequences of the valid ORF clones are available , and havInsertions or deletions that retained the ORF were observed in five clones. None of these significantly altered the ORF, as four cases involved three bases while one involved 12 bases. We also observed a polymorphism in MSE55 which involved the insertion or deletion of six amino acid repeat units and exists in three different alleles. We amplified and sequenced genomic DNA fragments across this region from 152 chromosomes of European ancestry and found that all three alleles are common and in Hardy-Weinberg equilibrium. In this case the clone chosen for the ORF collection was the same allele as seen in the publicly available genomic sequence.APOL4 contains a 2-bp insertion compared to the canonical genomic sequence annotation. This results in a frameshift that substantially extends the ORF from 127 amino acids to 348 amino acids. We designed a PCR reaction to directly interrogate the insertion/deletion and sequenced 144 chromosomes of European ancestry. Both alleles are common in this population, and are in Hardy-Weinberg equilibrium, with the 348-amino acid form being the minor allele at 46.5%. For bK216E10.6 we obtained an ORF clone with a 2-bp insertion compared to the genomic annotation, which results in an ORF that contains an extra 318 amino acids. Using the same strategy we sequenced 150 chromosomes and showed that the sequence producing the shorter peptide is the minor allele with a frequency of 20%, and the alleles are again in Hardy-Weinberg equilibrium. In this case we do not have an accepted clone, as the insertion increased the ORF length beyond the primer sequence. The third gene is TXN2 which shows a 2-bp insertion compared to the genomic sequence which is also found in an EST (AA586375), but has not been studied further. An insertion/deletion polymorphism that alters the ORF has previously been observed in MICA on chromosome 6 [In three cases we obtained clones with insertion/deletion polymorphisms that altered the ORF but were supported by available chromosome 22 sequence. To determine whether to accept these clones as ORF cDNAs, we examined all three in more detail. The clone obtained for gene In addition, we were able to amplify a PCR fragment which could be identified as originating from the correct gene for an additional 53 ORFs, but have not yet been able to obtain an acceptable clone because of the presence of unconfirmed bases, or problems with splice forms including partially processed transcripts. In most cases, only one or two amino acids are changed, which could make these clones usable under some circumstances, perhaps after site-directed mutagenesis. It is also possible that these are rarer SNPs that are not currently present in dbSNP. This suggests that by sequencing more examples we will be able to obtain clones for these ORFs in the near future. Thus the clone collection would cover 83% (331) of the targeted ORFs.In total, we initiated the amplification and cloning process 538 times, excluding initial pilot trials. These 538 events break down as follows. For 180 (45%) targeted ORFs an acceptable clone was generated at the first attempt. Further rounds of clone-picking, reamplification or primer redesign generated a further 99 acceptable clones, 83 clones containing an unconfirmed base alteration, 54 clones containing an alternative splice which lost the ORF, 23 clones containing a rearrangement or erroneous amplification event, 19 clones with retained intron sequences, four clones containing unresolved sequencing problems and 36 clones which were not the expected gene. For 41 genes we were unable to amplify a suitable product or failed to clone the fragment. Hence the efficiency of the process in terms of the return of acceptable clones is approximately 52% (278/540).p < 0.0001), although logistic regression indicates there is no significant interaction between them. This suggests that alternative amplification protocols using different polymerases or PCR additives might result in additional ORFs being obtained. However, we have tested three additional enzymes or mixes (Pfu Ultra (Stratagene), Phusion (Finnzymes) and Expand 20 kb+ PCR (Roche)) and additives including DMSO, glycerol and betaine so far without identifying a design that solves the problem.A significant area of concern is where we were unable to generate a PCR product at all corresponding to the targeted gene. To find explanations for this type of failure, we examined both the sequence characteristics of the targeted ORF and elements of the experimental design. First we examined the crude differences between the classes of ORFs that we could and could not amplify. Figure Next, we explored whether it was possible to amplify any part of the failed target cDNAs from the universal mix. For 51 of the genes where we failed to amplify the expected fragment, we designed additional nested oligonucleotide primer pairs to amplify a short (100-274 bp) sequence across a splice junction. In 39 cases (74%) we amplified a fragment of the correct size and sequence under our standard nested PCR conditions, suggesting that template is present in the cDNA mix for these ORFs (data not shown). Therefore, in most cases it is possible to amplify part of the targeted ORF from the cDNA mix using this protocol, indicating that the level of target in the mix is not limiting in these cases. Given that we know we can amplify parts of many of the problematic genes, one variation that could improve access to larger ORFs in the future would be to amplify larger transcripts in pieces that can then be reassembled into a single clone using appropriate restriction enzyme digestion and ligation or PCR cloning methods.et al. [We also examined whether successful amplification was biased towards genes expressed in many tissues. Su et al.  have genet al.  and wereNlaIII SAGE libraries downloaded from the SAGEmap resource [p = 0.84). Furthermore, because the SAGE tag data also gives an indication of expression level, we examined whether the mean expression level found by SAGE  affected probability of expression and again found no significant relationship (Mann Whitney test, p = 0.79). Taken together these analyses indicate that the success of our amplification strategy is not significantly influenced by either the range of tissues in which a gene is expressed or the level of expression. Clearly there will be some genes expressed at low levels, at specific times or in specific tissues that will need special treatment, but these data suggest that these cases may be few.We also examined diversity of expression by analyzing serial analysis of gene expression (SAGE) data derived from 242 Returning to the cDNA clone collections, of the 331 targeted genes for which we can obtain either an acceptable clone (278) or a clone of the correct ORF but currently with a problem in its sequence (53), 208 genes also have clones in the cDNA clone collections we analyzed; 123 genes only have clones in the new chromosome 22 ORF set described here. In addition, for 19 genes which are represented in the cDNA clone collections we were unable to isolate a clone Figures , 3. Thisp < 0.001) for genes not isolated (NOT) as compared to those isolated by us (SANGER) or those isolated both by us and the cDNA collections (BOTH). This suggests, as expected, that longer ORFs are harder to amplify or clone. A significant influence (p < 0.05) was also found for higher GC content in the genes that were either not isolated (NOT) or found only in the cDNA collections (OTHER) compared with the SANGER or BOTH classes, reflecting the influence of GC content on the ability to amplify a cDNA target as discussed above. The only significant difference (p < 0.05) for diversity of expression was between genes cloned only by us (SANGER) and those present in both our set and the cDNA collections (BOTH), with less diversely expressed genes slightly enriched in the SANGER class. This result was seen only in the microarray data, although the effect was also present in the SAGE data at just below significance. This suggests that the method described here may be able to access less widely expressed genes than have been sampled by existing cDNA library sequencing, although the effect is small. Finally, analysis of the mean level of expression of the genes in the four classes based on the normalized SAGE tag count showed no significant difference, indicating that level of expression is not a significant factor for this set of genes.We analyzed the four classes of genes (isolated by us and in the cDNA collections (BOTH), isolated only here (SANGER), isolated only by the cDNA collections (OTHER) and not isolated (NOT)) by GC content, length and diversity of expression as defined above for microarray data and SAGE using nonparametric analysis of variance  that we could not obtain are present in the cDNA collections analyzed, and when these are included, the overall coverage of the known full-length protein-coding genes reaches 88%. While this represents a substantial gain over cDNA sequencing alone, it is clear that complete coverage may require further modification of the approach or additional strategies as well.The quality control that is introduced by starting with annotated genes on the genomic sequence allows identification of SNPs and artifacts within the clones, and allows confirmation or rejection of each clone as it is generated. The checking process also provides verification of gene structures annotated from assembled ESTs, and in a few cases revealed errors. Our approach also has some advantages for scale-up to whole genomes. The starting point is a single PCR reaction using a universal template, which could be adapted to standard automation platforms. Subsequent steps, including ligation, transformation, clone picking, sequencing and sequence analysis, are all amenable to existing robotic approaches or automation. At present, the gel-purification step of the amplified PCR fragment might be difficult to automate. It is also likely that the final sign-off on the sequence alignment of clones will require human intervention in much the same way as finishing genomic sequences does. However, application to whole genomes demands a high-quality gene annotation to be available for the whole genome.We have generated a set of quality-controlled ORFs surrounded by a short stretches of 5' and 3' untranslated sequence in a uniform vector. The ORF portions of these intermediary clones are currently being amplified and subcloned in frame into a mammalian expression vector which fuses the amino-terminal T7 phage major capsid protein to the amino or carboxy terminus of the protein. We have successfully performed subcellular localization studies using immunofluorescence microscopy with these clones. We are also transferring the ORFs into Gateway pDONR clones (Invitrogen) and subsequently using GFP fusion destination vectors for subcellular localization. The availability of the ORF in a generic vector provides flexibility in the future downstream formats in that the endogenous Kozak sequence and the translation start and stop are maintained, and without additional amino acids from recombination sites. Finally, it is worth noting that this approach could also be applied to amplifying and cloning the many alternatively spliced forms of genes, or ORFs from different individuals or haplotypes. The ability to access the many additional variants beyond the canonical ORFeome could prove a valuable tool for future studies.cDNA sequences were downloaded from the websites of the following publicly available cDNA collections in January 2004. For the Mammalian Gene Collection  or Pfu DNA polymerase (Stratagene) using the manufacturers' recommended cycling profiles for 30 or 35 cycles in a 25 μl reaction. Fragments were then diluted 1 in 50 with sterile water and 5 μl used as template for a second 25 μl amplification using the inner primer pair . Additional enzymes including Pfu Ultra (Stratagene), Phusion (Finnzymes) and Expand 20 kb+ PCR (Roche) were trialed according to the manufacturers' recommendations. Fragments of the expected size were gel-purified, extracted with QIAquick spin columns (Qiagen), 3'-tailed with an adenosine residue using Amplitaq polymerase (Perkin Elmer) and subcloned using the pGEM-T Easy Vector System (Promega). Sequencing template was prepared either by plasmid miniprep or, in the majority of cases, by amplifying clone inserts with vector primers and cleaning the amplified fragment with either QIAquick Gel Extraction Kit or Shrimp alkaline phosphatase (1 unit, Amersham) and ExonucleaseI (1 unit, Amersham) (see below). Sequencing was performed with BigDye terminator v3 Cycle Sequencing Kits (Applied Biosystems) using vector primers, the inner nested primer pair and pairs of primers designed at 600-base intervals along the predicted gene sequence. Sequence was assembled using the contig assembly program CAP3 [Chromosome 22 gene annotations containing full-length ORFs, as defined in Collins et al. , but notet al. ) and Perram CAP3 , alignedThe 398 annotated ORF sequences were matched by blastn (version 2.0 MP-WashU, 10 April 2004 ) to cDNAMicroarray data indicating the distribution of expression for many human genes over 47 tissues using the Affymetrix human U95A array  was downFor SAGE data, the 398 target ORF sequences were matched by blastn  against Homo sapiens NlaIII library (GPL4) were determined. Tag counts were normalized to tags per million for each library, and then averaged to give a mean expression level. Diversity of expression was defined as the number of libraries in which a tag occurred.A Perl program was then used to search these files for SAGE tags mapping to each UniGene cluster and the tag counts for each 2, 67 mM Tris-HCl, 16.7 mM (NH4)2SO4 pH 8.8, 170 μg/ml BSA, 10 mM 2-mercaptoethanol, 500 μM each dATP, dCTP, dGTP, dTTP, 0.04 units/μl Amplitaq, 0.75 μM each primer, 10.13% sucrose, 0.0029% Cresol Red . Reactions were cycled in an MJ thermocycler at 94°C for 5 min, followed by 35 cycles of 30 seconds at 94°C; 30 sec at 65-66°C; 30 sec at 72°C, followed by a final 72°C for 5 min. PCR reactions were treated with 1 unit of shrimp alkaline phosphatase and 1 unit of exonuclease I in reaction buffer supplied by the manufacturer (USB, 10 × buffer - 200 mM Tris-HCl pH 8, 100 mM MgCl2) for each 10 μl PCR reaction. Reactions were heated at 37°C for 30 min followed by 80°C for 15 min to inactivate the enzymes. PCR products were then sequenced from both ends using the primers used from the amplification step and BigDye terminator v3 Cycle Sequencing Kits (Applied Biosystems). Sequences were analyzed using GAP4 [Fifty nanograms of genomic DNA from 78 unrelated individuals (ECACC Human Random Control Panel) was amplified in 15 μl reactions containing: 6.7 mM MgCling GAP4 .p-values are given after Dunn's multiple comparisons test. Additional data file The following additional data files are available with the online version of this article. Additional data file The 278 successfully cloned ORFsClick here for additional data fileEnzymes and templates used to amplify ORFsClick here for additional data fileThe sequence variation between ORF clone and genomic sequenceClick here for additional data fileThe nested oligonucleotide primers designed for the 398 targeted genesClick here for additional data fileThe results of nonparametric ANOVA  for chromosome 22 genes isolated as cDNA by the method described here only (SANGER), found in the cDNA collections only (OTHER), isolated by both ourselves and the cDNA collections (BOTH) or not isolated (NOT)Click here for additional data fileA list of the 278 cloned ORFsClick here for additional data fileA list of the 398 target ORFsClick here for additional data file