Arabidopsis thaliana, unique functions for three DCL proteins in miRNA (DCL1), endogenous siRNA (DCL3), and viral siRNA (DCL2) biogenesis were identified. One RDR protein (RDR2) was required for all endogenous siRNAs analyzed. The loss of endogenous siRNA in dcl3 and rdr2 mutants was associated with loss of heterochromatic marks and increased transcript accumulation at some loci. Defects in siRNA-generation activity in response to turnip crinkle virus in dcl2 mutant plants correlated with increased virus susceptibility. We conclude that proliferation and diversification of DCL and RDR genes during evolution of plants contributed to specialization of small RNA-directed pathways for development, chromatin structure, and defense.Multicellular eukaryotes produce small RNA molecules (approximately 21–24 nucleotides) of two general types, microRNA (miRNA) and short interfering RNA (siRNA). They collectively function as sequence-specific guides to silence or regulate genes, transposons, and viruses and to modify chromatin and genome structure. Formation or activity of small RNAs requires factors belonging to gene families that encode DICER (or DICER-LIKE [DCL]) and ARGONAUTE proteins and, in the case of some siRNAs, RNA-dependent RNA polymerase (RDR) proteins. Unlike many animals, plants encode multiple DCL and RDR proteins. Using a series of insertion mutants of  In plants, RNA-mediated silencing pathways have diversified in unique ways. This study elucidates the specific functions of some of the key regulators in development, chromatin structure, and pathogen defense Arabidopsis), which catalyzes accurate excision of the mature miRNA  (RDR6 is necessary for sense transgene-mediated RNAi, but not for silencing of constructs that encode transcripts with hairpins containing extensive dsRNA structure (Arabidopsis contains four DCL genes (DCL1 to DCL4), only one of which (DCL1) has been assigned a definitive function in small RNA biogenesis  . RDR6 istructure . In manytructure , althougtructure . Arabidoogenesis . Biochemogenesis . Endogenogenesis . Exogenoogenesis .Both siRNAs and miRNAs function post-transcriptionally to suppress or inactivate target RNAs. siRNAs guide sequence-specific nucleolytic activity of the RNA-induced silencing complex (RISC) to complementary target sequences . Among oSchizosaccharomyces pombe and Arabidopsis, endogenous siRNAs from repeated sequences corresponding to centromeres, transposons, and retroelements are relatively abundant   . And, se] locus) . The RNA] locus) .Arabidopsis. Plants with point mutations or insertions in several members of the DCL and RDR gene families were examined. The data indicate that plants genetically diversified several factors involved in formation of functionally distinct small RNAs.Using a genetic approach, we show here the existence of three small RNA-generating pathways with unique requirements in Arabidopsis miRNA formation. As shown for miR-171, miR-159  and DCL3 (At3g43920) genes, respectively (DCL2 and DCL3 transcripts accumulated to detectable levels in inflorescence tissues, but not in leaves. The mutant dcl2-1 and dcl3-1 transcripts were not detected in either tissue type (rdr1-1 and rdr2-1 mutants contained T-DNA insertions in RDR1 (At1g14790) and RDR2 (At4g11130), respectively -treated leaves, as shown previously  was analyzed in parallel with the rdr1 and rdr2 mutants. This rdr6-1 mutant displayed a weak virus-susceptibility phenotype that was consistent with previously reported sde1 and sgs2 mutants (RDR6 transcript levels were detected between wild-type and rdr6-1 mutant plants (data not shown).At least two factors, DCL1 and HEN1 (HUA ENHANCER1), are involved in  miR-159 A, and se miR-159 , mutantsulations B. Plantsleotides  . The siRNA sequences were identified at 5,299 genomic loci , 5S rDNA repeats, and nonrepetitive sequence was calculated C. siRNAsAtSN1) was selected for detailed analysis. AtSN1-derived siRNA formation requires AGO4 . The endogenous siRNA requirement for RDR2 contrasted with the miRNAs, which exhibited complete insensitivity to each of the rdr mutations tested (see Each siRNA population was eliminated in the tant see B. In prested see B. These hen1-1 mutant. Two of the siRNA populations, siRNA1003 and the AtSN1-siRNAs, were reduced to undetectable levels in hen1-1 plants (see hen1-1 plants compared to wild-type La-er plants. Thus, each type of endogenous siRNA tested requires DCL3 and RDR2, but only the highly repeated 5S rDNA and retroelement-derived siRNAs require HEN1. In fact, the requirement for, or independence from, HEN1 was precisely the same as AGO4 at each of these loci (D. Zilberman and S. Jacobsen, unpublished data).The HEN1 protein was implicated in post-transcriptional silencing of sense-, but not hairpin-forming, transgenes . We testants see B. The siAtSN1 siRNA accumulation and methylation of cytosine positions at the AtSN1 locus , CpNpG (43.1%), and asymmetric CpHpH (16.3%) methylation in Col-0 wild-type plants  was unaffected , whereas sensitivity to MspI indicates lack of methylation at only CpNpG sites. In wild-type Col-0 and La-ing MspI B. In rdrAtSN1 in rdr2-1 and dcl3-1 mutant lines. Loci containing genes encoding a retrotransposon reverse transcriptase and phosphofructokinase β subunit (PFK) were used as positive controls for sequences associated primarily with K9- and K4-methylated histone H3, respectively (AtSN1, decreased levels of histone H3K9 methylation were detected in both rdr2-1 and dcl3-1 mutants (see rdr2-1 relative to dcl3-1 plants. Little or no change in H3K4 and H3K9 methylation was detected at the control loci. In addition, no changes in H3K4 or H3K9 methylation were detected at AtSN1 in cmt3-7 plants (data not shown). The changes in H3 methylation shown here are similar to those at several heterochromatic or silenced loci in ago4 mutant plants (Chromatin immunoprecipitation (ChIP) assays were used to detect changes in H3K4 and H3K9 methylation at ectively . At AtSNants see C. This wants see C. The ext plants .AtSN1-derived transcripts was measured in rdr2-1 and dcl3-1 mutant plants and compared against the level of PFK transcript using semiquantitative RT-PCR. As shown in AtSN1 transcripts were detected in wild-type Col-0 plants. However, the normalized level of AtSN1 transcripts was over 8- and 3-fold higher in rdr2-1 and dcl3-1 mutant plants, respectively, compared to wild-type plants. Therefore, loss of siRNA-forming capability correlated with loss of heterochromatic marks and elevated transcript levels at an endogenous locus that is normally silenced at the chromatin level.The level of Nicotiana benthamiana, using an Agrobacterium infiltration assay. Subcellular accumulation sites for these proteins were compared to those of β-glucurodinase (GUS)–GFP (cytosolic control) and nuclear inclusion a protein (NIa)–GFP (nuclear control). The DCL3–GFP, HEN1–GFP, and GFP–AGO4 fusion proteins were detected exclusively in the nucleus  fusions in a heterologous plant,  nucleus , indicatdcl mutant series. Two of the viruses, a GFP-tagged version of turnip mosaic virus (TuMV–GFP) and turnip crinkle virus (TCV), infect Arabidopsis systemically and cause moderate to severe disease symptoms. The third virus, cucumber mosaic virus strain Y (CMV-Y), infects plants systemically, but causes only mild symptoms. Wild-type (Col-0 and La-er) and mutant plants were inoculated on rosette leaves, and upper, noninoculated tissue (cauline leaves and inflorescences) was analyzed for virus-specific siRNAs at 7 and 14 d post-inoculation (dpi).The involvement of DCL1, DCL2, and DCL3 in siRNA formation in response to infection by three dissimilar RNA viruses was tested using the dcl1-7, dcl2-1, and dcl3-1 mutant plants, siRNAs accumulated to levels that were similar to those in infected wild-type plants at 7 and 14 dpi  or significantly higher than (p < 0.05, 14 dpi) the titers in wild-type plants  lower compared to infected wild-type plants  class. While DCL3 undoubtedly functions as the ribonuclease to process dsRNA precursors, RDR2 presumably functions as a polymerase to form dsRNA molecules de novo using templates resulting from transcription of DNA. At some loci, however, RDR2 may be unnecessary as a catalytic subunit, but rather contribute to the formation or stability of a complex that contains active DCL3. This could be the case at some sites, such as the siRNA02 locus, that contain inverted duplications and that may form transcripts with extensive dsRNA structure. Interestingly, accumulation of siRNAs specific to a hairpin construct was shown to be RdRp dependent in fission yeast . At somedcl2-1 plants, which exhibited delayed viral siRNA accumulation and increased susceptibility and sensitivity. However, there are several reasons to suspect that multiple antiviral, siRNA-generating systems exist. siRNAs triggered by TCV were not eliminated in dcl2-1 plants, but rather siRNA accumulation was delayed. Although this could be due to incomplete loss of DCL2 function in the mutant, it could also reflect the existence of secondary or redundant DCL activities. Among the three viruses tested, two were unaffected by the dcl2-1 mutation. This strongly implies the existence of one or more other siRNA-generating activities with unique or redundant antiviral specificity. Further, the DCL2-dependent system may have functions in addition to those associated with antiviral defense. The DCL2–GFP fusion protein was detected primarily in the nucleus, whereas TCV replicates and accumulates outside of the nucleus. Experiments to determine the genetic requirements for RDR1 and RDR2 during antiviral silencing against the three viruses were inconclusive, again possibly the result of functional redundancies or the presence of confounding viral RdRp activities (rdr6 (sde1/sgs2) mutants were deficient in CMV-induced silencing. Additionally, RDR1 contributed to defense against tobamoviruses.The third system functions in antiviral defense and involves DCL2. Loss of this system was specifically detected in TCV-infected tivities . Mourraitivities , on the DCL gene family, the members do not correlate one-for-one with those in Arabidopsis (Z. Xie and J. Carrington, unpublished data). Further study is required to correlate the DCL activities from wheat germ with those in Arabidopsis.Caenorhabditis elegans and human, for example, contain only one DICER  accumulate to such low levels in ago4 and hen1 mutants? One possibility is that heterochromatic marks (DNA and H3K9 methylation) and associated factors serve to recruit RDR2, DCL3, or both to specific sites on chromatin, thus establishing a reinforcement loop. Loss of heterochromatin in an ago4 mutant, for example, would result in failure to recruit the siRNA-generating enzymes to transcripts originating from a target locus and, therefore, the absence of siRNAs. This hypothesis, however, does not hold for some other siRNA-generating sites, such as those that yield cluster2 siRNAs and siRNA02. Accumulation of siRNAs from these sites is unaffected or even enhanced in ago4 and hen1 mutants. In wild-type plants, these loci are both hypomethylated at CpG and non-CpG sites and are associated with histone H3 that largely lacks K9 methylation (data not shown). The siRNAs formed from these loci clearly require RDR2 and DCL3, but they appear not to affect chromatin structure. These siRNAs may be sequestered elsewhere in the cell and unable to interact with chromatin or chromatin-associated factors.Accumulation of siRNA from endogenous loci and transgenes does not necessarily require AGO4 (D. Zilberman and S. Jacobsen, unpublished data), suggesting that AGO4 acts downstream of siRNA formation to direct DNA methylation. Losses of AGO4 and HEN1 have nearly identical effects on all siRNAs tested, possibly because HEN1 and AGO4 affect a similar point in the pathway. If AGO4 and HEN1 function downstream of siRNA formation, why do siRNAs derived from some sites  of DCL2 (At3g03300). dcl3-1 has a T-DNA insertion within predicted exon 7 of DCL3 (At3g43920) at a point 2,136 nucleotides beyond the ATG in genomic DNA. This introduces four codons after the serine 288 codon, followed by a premature stop codon. rdr1-1 has a T-DNA insertion within predicted exon 1 after nucleotide 2,366 beyond the ATG of RDR1 (At1g14790). rdr2-1 has a T-DNA insertion within predicted exon 1 (in front of nucleotide 316 from the ATG) of RDR2 (At4g11130). rdr6-1 has a T-DNA insertion within predicted exon 2 (in front of nucleotide 3,977 from ATG of the genomic DNA) of RDR6 . Each insertion line was backcrossed twice to Col-0 and brought to homozygosity. Additional information about the insertion lines are provided in the supplemental online materials.All plants were grown under standard greenhouse conditions. The eviously . Other mdcl1-7, hen1-1, ago4-1, and cmt3-7 mutants, La-er was the wild-type control.For analysis of each insertion mutant, Col-0 was the wild-type control plant. For Arabidopsis inflo-rescence tissue was used for miRNA and endogenous siRNA analysis. Probes for miR-171 and AtSN1-siRNA analysis were described previously  was treated with sodium bisulfite and amplified using primers AtSN1-BS1 (5′-GTTGTATAAGTTTAGTTTTAATTTTAYGGATYAGTATTAATTT-3′) and AtSN1-BS2 (5′-CAATATACRATCCAAAAAACARTTATTAAAATAATATCTTAA-3′). At least 18 independent clones were sequenced for each genotype.Cytosine methylation was analyzed by bisulfite sequencing of genomic DNA or by DNA blot assay following digestion with methylation-sensitive restriction endonucleases, as described elsewhere . The regAtSN1 in wild-type Col-0 and rdr2-1 and dcl3-1 mutants was measured relative to that at internal control loci, At4g04040 and At4g03800. The data were then normalized against the values measured in Col-0.ChIP assays were done using antibodies specific for dimethyl-histone H3K4 (Upstate Biotechnology, Lake Placid, New York, United States) or dimethyl-histone H3K9 (kindly provided by T. Jenuwein, Research Institute of Molecular Pathology, Vienna, Austria) as described elsewhere . Methyla35S:DCL3–GFP construct contained the DCL3 coding region fused to GFP coding sequence, flanked by the cauliflower mosaic virus (CaMV) 35S promoter and terminator sequences. The expression cassette was cloned in pSLJ755I5. All other GFP fusion constructs were made by cloning the coding sequence into pGWB5  or pGWB6 , a set of gateway-compatible binary vectors designed for 35S promoter-driven expression of GFP fusion proteins (kindly provided by T. Nakagawa, Shimane University, Izumo, Japan). Cloning using gateway vectors was done using reagents and protocols from Invitrogen . Constructs were introduced into Agrobacterium tumefaciens strain GV2260 and expressed in N. benthamiana leaves as described previously (The eviously . Fusion Arabidopsis plants (approximately 4 wk old, prior to bolting) were infected with TuMV–GFP, CMV-Y, and TCV as described previously (Wild-type and mutant eviously . At 7 aneviously .Arabidopsis genome was done using RepeatMasker (http://ftp.genome.washington.edu/RM/RepeatMasker.html) and Repbase (http://www.girinst.org/index.html).Computational identification of repeat sequences, including transposons and retroelements, in the Arabidopsis siRNAs and miRNAs, including those that were analyzed in this work, can be found in the Arabidopsis Small RNA Project database .Further information about Figure S1Arabidopsis DCL and RDR genes and location of T-DNA insertion sites in mutant lines.(A) Exon (bars)/intron (lines) organization of the DCL2 and DCL3 mRNA in Col-0 and the respective mutants. DNA fragments corresponding to nucleotides 2,652–3,292 of the DCL2 open reading frame and nucleotides 2,805–3,571 of the DCL3 open reading frame were used as hybridization probes. As a control, the blots were stripped and hybridized with a β-tubulin-specific probe ((B) RNA blot analysis  for ic probe .RDR1 and RDR2 mRNA in Col-0 and the respective mutants. DNA fragments corresponding to nucleotides 2,900–3,300 of the RDR1 open reading frame and nucleotides 10–271 of the RDR2 open reading frame were used as gene-specific probes. RNA samples from SA-treated leaf tissues were also included in the analysis.(C) RNA blot analysis  for (5.9 MB EPS).Click here for additional data file.Figure S2N. benthamiana using an Agrobacterium-injection procedure. Leaf tissue from injected zones was excised at 2 dpi for immunoblot assay using a monoclonal antibody against GFP and confocal microscopy (see The 35S promoter-driven GFP fusion constructs were transiently expressed in copy see . An arro(10.8 MB EPS).Click here for additional data file.Table S1(25 KB DOC).Click here for additional data file.Table S2(24 KB DOC).Click here for additional data file.http://www.ncbi.nlm.nih.gov/Genbank/) accession numbers for the entities discussed in this paper are At1g14790 (NM_101348), At3g03300 (NM_111200), At3g43920 (NM_114260), At3g49500 (NM_114810), At4g11130 (NM_117183), chromosome I (NC_003070.3), chromosome III (NC_003074.4), and siRNA02 (AF501743).The GenBank  accession numbers for the T-DNA insertion lines discussed in this paper are rdr1-1 (SAIL_672F11), rdr2-1 (SAIL_1277H08), and rdr6-1 (SAIL_388H03).The SAIL (formerly Garlic)  accession numbers for the T-DNA insertion lines discussed in the paper are dcl2-1 (SALK_064627) and dcl3-1 (SALK_005512).The SIGnAL database (


Epidemiological investigations of infectious disease are mainly dependent on indirect contact information and only occasionally assisted by characterization of pathogen sequence variation from clinical isolates. Direct sequence analysis of the pathogen, particularly at a population level, is generally thought to be too cumbersome, technically difficult, and expensive. We present here a novel application of mass spectrometry (MS)–based technology in characterizing viral sequence variations that overcomes these problems, and we apply it retrospectively to the severe acute respiratory syndrome (SARS) outbreak in Singapore.The success rate of the MS-based analysis for detecting SARS coronavirus (SARS-CoV) sequence variations was determined to be 95% with 75 copies of viral RNA per reaction, which is sufficient to directly analyze both clinical and cultured samples. Analysis of 13 SARS-CoV isolates from the different stages of the Singapore outbreak identified nine sequence variations that could define the molecular relationship between them and pointed to a new, previously unidentified, primary route of introduction of SARS-CoV into the Singapore population. Our direct determination of viral sequence variation from a clinical sample also clarified an unresolved epidemiological link regarding the acquisition of SARS in a German patient. We were also able to detect heterogeneous viral sequences in primary lung tissues, suggesting a possible coevolution of quasispecies of virus within a single host.This study has further demonstrated the importance of improving clinical and epidemiological studies of pathogen transmission through the use of genetic analysis and has revealed the MS-based analysis to be a sensitive and accurate method for characterizing SARS-CoV genetic variations in clinical samples. We suggest that this approach should be used routinely during outbreaks of a wide variety of agents, in order to allow the most effective control. Mass spectrometry-based sequence analysis provides a sensitive and accurate method to characterize genetic variation of the SARS coronavirus in clinical samples During infectious disease outbreaks, due to either new or established agents, extensive information gathering is required to enable identification of the source, transmission routes, and the effect of containment policies. It is becoming increasingly clear that traditional approaches based on travel and contact tracing are not sufficient for tracking an outbreak. New sequence-based techniques for pathogen detection and identification have the potential to become perhaps the most important component of these investigations, as demonstrated in the recent worldwide effort in fighting the epidemic of severe acute respiratory syndrome (SARS). The discovery of the SARS coronavirus (SARS-CoV) as the etiological agent for SARS was a major breakthrough , which wDirect sequence analysis of a pathogen in a large number of clinical samples, particularly at a population level, is generally cumbersome, technically challenging, and expensive. Therefore, a rapid, sensitive, high-throughput, and cost-effective screening method would greatly facilitate large-scale characterization of genetic variation of pathogens at a population level. A mass spectrometry (MS)–based method for detecting single nucleotide polymorphisms has been routinely used as a high-throughput method for genotyping human samples, and, thus, we sought to extend this methodology to detect pathogen sequence variations. Here, we demonstrate the high sensitivity of the MS-based analysis in detecting SARS-CoV sequence variations and apply it to analyzing both cultured viral isolates and uncultured tissue samples of SARS-CoV.The SARS-CoV samples used in the sensitivity study were previously described . BrieflyVero cell cultured viral isolates were obtained from 13 Singapore patients: the presumed index case (patient Sin2500) of the Singapore SARS outbreak, whose date of illness onset was 25 February 2004; three primary contacts (patients Sin2677, Sin2774, and Sin2748), whose dates of onset were 9 March 2004 (Sin2677 and Sin2774) and 14 March 2004 (Sin2748); one secondary contact (patient Sin2679), who was believed to have contracted SARS from index patient Sin2500 through another primary contact not included in this study and whose date of illness onset was 15 March 2004; and another eight patients (Sin842, Sin845, Sin846, Sin847, Sin848, Sin849, Sin850, and Sin852), who were believed to be the fifth- or six-generation contacts of index patient Sin2500 (based on contact tracing records) and whose dates of illness onset ranged from 2 April to 14 April 2004. All the patients fitted the World Health Organization case definition for probable SARS [For the spiked human blood samples, RNA was extracted from 200 μl of blood into 50 μl of water using a HighPure RNA kit (Roche, Basel, Switzerland). For the clinical samples, RNA was extracted into 30 μl of water using a QiAmp viral RNA mini kit . RNA samples were reverse transcribed into cDNA using 2 μl of RNA as template, a SuperScript kit , and 13 sequence-specific primers . All cDNReal-time quantitative PCR analyses of the RNA samples from spiked human blood were performed using a Lightcycler Sars-CoV quantification kit (Roche). Each analysis was done using 1 μl of RNA and in accordance with the manufacturers' instructions.Twenty-one single nucleotide variations (SNVs) of SARS-CoV were analyzed in this study. Eight of these were identified from our previous sequence analysis of five Singapore SARS-CoV viral isolates and represent variations , and 13 A primer extension genotyping assay was designed for each SARS-CoV SNV using SpectroDesigner software  and analyzed using the MassARRAY system (Sequenom) and the recommended protocol for the MADLI-TOF (matrix-assisted laser desorption ionization/time-of-flight) MS-based genotyping analysis . One micThe analytical detection limit of the combined RNA preparation/RT-PCR/MALDI-TOF MS system was determined by probit analysis  using th6 (SB1), 3.84 × 103 (SB2), 2.17 × 103 (SB3), 6.21 × 102 (SB4), and 1.20 × 102 (SB5) copies per microliter , 12 of the 15 analyses of the SB4 sample , and six of the 15 analyses of the SB5 sample . According to probit analysis, this corresponds to a 95% probability of detection when at least 75 copies of viral RNA  are present per reaction, and still a 50% detection chance at 38 copies of virus RNA per reaction . All 64 sham spiked samples gave negative genotype calls.Having demonstrated the high sensitivity of the MS-based analysis, we typed 18 SARS-CoV SNVs in the cultured viral isolates from nine Singapore patients, including five early Singapore cases (Sin2500, Sin2774, Sin2748, Sin2677, and Sin2679) and four later Singapore cases (Sin842, Sin848, Sin849, and Sin852).Of the 18 SNVs analyzed, nine were detected in at least two SARS isolates . AssuminIn addition, the sequence variant sharing pattern at SNVs 28,008, 548, 1,727, and 13,347 can further differentiate the four fifth- and six-generation isolates into three different sub-lineages  defined The molecular relationship among the viral isolates derived from MS-based viral sequence analysis is consistent with the one derived from the whole-genome sequence analysis of the same isolates , clearlyWe also typed the 18 SNVs in four uncultured lung tissue samples from patients Sin842, Sin848, Sin849, and Sin852, and compared their sequence variations with their matched cultured isolates .Of the 72 direct sequence variation comparisons (18 SNVs in four sample pairs), nine differences were identified. Six of these were due to heterogeneous sequences in primary tissue samples. For example, the cultured isolate from patient Sin849 showed the sequence variant T:T:C at SNV positions 548, 1,727, and 13,347, a subset of the heterogeneous T/C:T/C:T/C variant observed in the matched lung tissue sample from the same patient. Direct comparison of the MS spectrums of the three different sequence variants (T, T/C, and C) at SNV position 1,727 in In addition, another three sequence variant differences were observed between the paired cultured and tissue samples from patient Sin848 at SNV positions14,807, 26,205, and 26,509, where the tissue sample showed the sequence variant C:C:T, but the matched culture isolate showed the variant T:T:C . In all The application of tagging and thus tracking of SARS-CoV strains using viral lineage- and/or strain-specific sequence variants was further demonstrated in our investigation of a clinical sample from a German patient. This patient stayed in Hanoi, Vietnam, before sharing an airplane flight with an early Singapore SARS patient on his way to New York via Frankfurt . German We genotyped the virus directly from a brochoalveolar lavage specimen from the German patient at four SNV positions (19,084, 23,792, 26,428, and 27,111) that were distinctive for the early Singapore SARS-CoV isolates see . The seqThrough the application of this MS mini-sequencing approach to the SARS outbreak in Singapore we have demonstrated the precision that pathogen sequence data can add to an epidemiological investigation. We analyzed 18 SARS-CoV SNVs and determined the molecular relationship among the viral isolates from 13 Singapore SARS patients see  from difA further application of MS-based viral sequence variation analysis in tracking the virus strain and thus the transmission of SARS-CoV was demonstrated by our confirmation of the Singapore origin of a SARS-CoV isolate from a German patient. Travel and contact tracing records for this German patient indicated more than one potential exposure to SARS-CoV, and because virus could not be cultured from the patient, it was difficult to pinpoint the origin of his infection by classical sequencing methods that require virus enrichment by culture. By genotyping the four SNV positions that showed unique variants in the early Singapore SARS-CoV isolates, we confirmed that this German patient was indeed infected by an early Singapore virus strain, most likely in a hitherto unnoticed aircraft transmission event from an early Singaporean patient who was later hospitalized with SARS in Germany . TherefoDirect viral sequence variation analysis of uncultured lung tissue samples identified cases of heterogeneous viral sequences in single patient samples. As the SARS-CoV virus is a single-strand RNA virus, the discovery of different sequences in a single tissue sample suggests the presence of multiple viral sequence variants, or quasispsecies, within the host when the sample was retrieved. Our result has further confirmed a recent observation of SARS-CoV quasispecies in individual patients  and is c3–104 RNA copies per milliliter), which is close to the detection limit of real-time RT-PCR based diagnostic tests  [Our study has clearly demonstrated the advantages of MS-based genetic analysis as a method for large-scale viral genetic characterization in clinical samples. Firstly, MS-based analysis has high sensitivity, providing successful detection of virus more than 95% of the time at virus concentrations as low as 75 RNA copies per reaction  ,21,22 aneaction) ,22,23,24MS-based sequence variation analysis is complementary to the identification of new sequence variation by direct sequence analysis and is particularly suitable for investigating agents for which there is already extensive sequence information. Direct sequence analysis is still the “gold standard” for identifying new sequence variations, but it is inefficient and is not necessary for characterizing known sequence variations in a large number of samples. A combination of initial characterization of genome sequence by direct sequence analysis in a subset of samples and the subsequent analysis of informative genetic variations via a MS-based approach is more efficient and suitable for large-scale population investigations. The genome sequences of a wide variety of pathogens and strains are being rapidly accumulated. In bacterial pathogens, strain sequence information is frequently limited to a relatively small number of genes , whereas in viruses such as influenza, extensive genomic knowledge is accumulating. Such accumulation of both partial- and whole-genome sequence information for pathogens will further extend the usefulness of this approach.In summary, our reassessment of the SARS-CoV transmission route in Singapore using MS-based viral sequence variation analysis highlighted the limitation of conventional epidemiological analysis based on travel and contact tracing, and the importance of informing clinical and epidemiological investigation of pathogen transmission by genetic analysis. With its demonstrated high throughput , sensitiFigure S16 copies per reaction (line a), 1.01 × 105 copies per reaction (line b), 9.4 × 103 copies per reaction (line c), 8.9 × 102 copies per reaction (line d), and 1.07 × 102 copies per reaction (line e). The virus loads determined in the five spiked human blood samples were as follows: SB1, 1.64 × 106 copies per reaction; SB2, 3.84 × 103 copies per reaction; SB3, 2.17 × 103 copies per reaction; SB4, 6.21 × 102 copies per reaction; and SB5, 1.20 × 102 copies per reaction. NTC, non-template control.The x-axis denotes the cycle number of the quantitative PCR assay, and the y-axis denotes fluorescence intensity (F2) over the background level. RNA standards were as follows: 1.05 × 10(244 KB DOC).Click here for additional data file.http://www.ncbi.nlm.nih.gov/Genbank/index.html) accession number for the Frankfurt-1 isolate is AY291315 and for the Urbani isolate is AY278741.The GenBank (Molecular biology (studying the makeup and function of molecules) is increasingly being used to track outbreaks of infectious diseases. For example, molecular biology can help to identify the cause of a disease (such as a virus, bacterium, or parasite) and understand how it spreads.These researchers had previously used molecular biology techniques to study different strains of the SARS virus, which causes the often fatal disease called severe acute respiratory syndrome. They had found that different strains could be distinguished from each other on the basis of specific genetic “fingerprints.” They now wanted to find quick and easy ways to determine the identity of particular viral strains found in sick patients.They used a molecular biology technique called mass spectrometry. They took samples from patients  and determined whether they could detect the SARS virus, identify specific strains, and distinguish between them.They found that mass spectrometry is a useful tool for detecting the SARS virus and for distinguishing between different strains. They also found that they could use this tool to help understand how the SARS virus had been transmitted between specific patients.For this technique to work, the researchers needed pre-existing information about genetic differences between strains. This means that detailed DNA sequencing is necessary to find these differences in the first place and to discover new ones as the virus evolves.The authors suggest that combining initial genetic sequencing of the different strains with the mass spectrometry technique to analyze subsequently large numbers of samples is the most efficient and cost-effective approach.Science Magazine:http://www.sciencemag.org/feature/data/sars/Public access Web pages on SARS from http://www.genomenewsnetwork.org/articles/05_03/sars_3.shtmlNews article on the SARS genome on the Genome Network News Web site: http://www.gis.a-star.edu.sg/homepage/default.jspGenome Institute of Singapore (GIS): http://www.gis.a-star.edu.sg/homepage/gismediapress.jsp?pid=19GIS's press release on differences between SARS strains:


The Wnt signaling pathway provides key information during development of vertebrates and invertebrates, and mutations in this pathway lead to various forms of cancer. Wnt binding to its receptor causes the stabilization and nuclear localization of β-catenin. Nuclear β-catenin then functions to activate transcription in conjunction with the transcription factor TCF. A recent report has challenged this basic precept of the Wnt signaling field, arguing that the nuclear localization of β-catenin may be unrelated to its function and that β-catenin functions at the plasma membrane to activate this signaling pathway. Here we present evidence that the pathway in fact does depend on the nuclear localization of β-catenin. We reexamine the functionality of various truncations of β-catenin and find that only the most severe truncations are true signaling-null mutations. Further, we define a signaling-null condition and use it to show that membrane-tethered β-catenin is insufficient to activate transcription. We also define two novel loss-of-function mutations that are not truncations, but are missense point mutations that retain protein stability. These alleles allow us to show that the membrane-bound form of activated β-catenin does indeed depend on the endogenous protein. Further, this activity is dependent on the presence of the C-terminus-specific negative regulator Chibby. Our data clearly show that nuclear localization of β-catenin is in fact necessary for Wnt pathway activation. An uncertainty about Wnt signalling -- and in particular about the role of beta-catenin -- is laid to rest Drosophila ortholog wingless (wg) is a segment polarity gene that defines posterior cell fates in each of the larval segments (for a review of the various functions of Wg, see Drosophila homolog of glycogen synthase kinase [GSK3β]). This complex is responsible for the retention of Armadillo (Arm) in the cytoplasm, for its phosphorylation, and thus for its targeting for ubiquitination and destruction. When the complex is inactivated by Dsh, the intracellular levels of Arm increase, and Arm enters the nucleus, where in combination with the transcription factor TCF/Pangolin, it activates the transcription of genes such as cyclin D and c-myc  causes activation of the Wnt pathway without entering the nucleus. However, this is not the first time that the controversy about the location of Arm/β-catenin function has arisen. Previously, a group working with amphibian embryos had found that membrane-tethered plakoglobin, a close relative of β-catenin, can activate Wnt signaling  is required for patterning.In this study, we reexamine Arm function using three classes of previously described arm gene were classified into three groups based upon their phenotypes and the position of stop codons that result in truncated proteins. The “weak” class has the smallest truncations and is represented by XM19arm. In germline clones  does not allow proper progression through oogenesis and germline clones do not make eggs (S18)  . We haveypes see G and 1H.8 . We haS18 has no effect on the cuticle of wild-type embryos  might reveal some signaling capacity of those alleles as well. Experiments of this kind have been difficult with ArmΔArm, given that it lacks the α-catenin-binding site and fails to rescue the junctional defect in “medium” and “strong” endogenous arm allele backgrounds. We have found that by expressing both ArmΔArm and ArmS18, we can recover intact embryos in all backgrounds tested. We find that “medium” and “weak” alleles can be induced to activate transcription, but the “strong” arm allele cannot .When Armignaling . Chan anignaling  suggest radation . Both muradation . Both muradation I and 5J.radation K and 5L.radation A and 5B.ΔArm is expressed in wild-type epidermis. If ArmΔArm functions independently of the endogenous protein, then all cells should assume the naked cell fate. However, this does not occur . Under these conditions, all cells in the cuticle take on the naked cell fate . Therefore, the activity of C-terminally truncated arm alleles in response to ΔArm expression suggests that, under certain conditions, removal of the C-terminus may actually enhance the transcriptional activity of Arm. One possibility is suggested by the recent discovery of Cby (ΔArm, we used RNA interference (RNAi) to reduce Cby levels in F1aarm embryos with and without ArmΔArm. In the absence of ArmΔArm, i.e., in embryos where most ArmF1a protein is cytoplasmic, Cby RNAi has no effect  weaker phenotypes, they are more effective at blocking the cell-fate transformation induced by Arm compare C and 5D.y of Cby , a nucleo effect D. Howeve compare B to 6C. ΔArm cannot activate transcription on its own. Based on these findings, we propose that membrane-tethered Arm, whether wild-type or activated, cannot activate transcription on its own. It does, however, have a profound effect on the endogenous form, forcing both “weak” and “medium” alleles to translocate to the nucleus and activate transcription. Our findings extend and build upon the original nuclear localization of Arm model  does not signal in the presence of uniform ArmΔArm , which we expressed in O43A01arm mutant embryos , consistent with the “medium” alleles retaining the TCF-binding region  for details on mutants used. Hypomorphic mutants of arm are as follows: LM134arm TCC to TTC at nucleotide 2776, F1aarm CGC to CAC at nucleotide 2990, XM19arm stop codon at nucleotide 3850, O43A01arm stop codon at nucleotide 3404, XP33arm stop codon at nucleotide 3466, XK22arm stop codon at nucleotide 3013. Other alleles used were S044230axin , M11–1zw3, V26dsh, Q8apc1, d40apc2, and IG22wg.The wild-type strain used was Oregon R. See Flybase /Y (zygotic)LM134 zw3M11–1arm /Y (zygotic)F1aarm /Y (zygotic)LM134arm /Y (zygotic)F1aarm /Y (zygotic); Arm–GAL4/UAS–ΔArm (zygotic)F1a zw3M11–1arm /Y (zygotic); Arm–GAL4/UAS–ΔArm (zygotic)LM134arm /Y (zygotic); Arm–GAL4/UAS–ΔArm (zygotic)LM134 zw3M11–1arm /Y (zygotic); Arm–GAL4/UAS–ΔArm (zygotic)O43A01arm /Y (zygotic)O43A01arm /Y (zygotic); ArmS18 (zygotic)F1aarm /Y (zygotic); Arm–GAL4/UAS–WIZ–Cby, UAS–ΔArm (zygotic)F1aarm /Y (zygotic); Arm–GAL4/UAS–WIZ–Cby (zygotic)XK22arm /Y (zygotic); ArmS18 XK22arm /Y (zygotic); MAT–GAL4/UAS–ΔArm (zygotic); ArmS18 S18, a mirystoylated, membrane-tethered full-length Arm . The antibodies used were anti-Arm , rabbit anti-Arm (Embryos were treated and stained as described previously , except anti-Arm b, rabbitanti-Arm . Though http://flybase.bio.indiana.edu).Embryos were lysed in extract buffer , and the extracts were separated by 7.5% SDS-PAGE and were blotted as described elsewhere a. MaternAccession Numbershttp://www.ncbi.nlm.nih.gov/Genbank/) accession numbers for the genes and alleles discussed in this paper are Q8apc1 (U77947), d40apc2 (AF091430), arm (X54468), S044230axin (AF086811), V26dsh (U02491), IG22wg (NM 164746), and M11–1zw3 (X54005).The GenBank (


The size distribution of gene families in a broad range of genomes is well approximated by a generalized Pareto function. Evolution of ensembles of gene families can be described with Birth, Death, and Innovation Models (BDIMs). Analysis of the properties of different versions of BDIMs has the potential of revealing important features of genome evolution.In this work, we extend our previous analysis of stochastic BDIMs.In addition to the previously examined rational BDIMs, we introduce potentially more realistic logistic BDIMs, in which birth/death rates are limited for the largest families, and show that their properties are similar to those of models that include no such limitation. We show that the mean time required for the formation of the largest gene families detected in eukaryotic genomes is limited by the mean number of duplications per gene and does not increase indefinitely with the model degree. Instead, this time reaches a minimum value, which corresponds to a non-linear rational BDIM with the degree of approximately 2.7. Even for this BDIM, the mean time of the largest family formation is orders of magnitude greater than any realistic estimates based on the timescale of life's evolution. We employed the embedding chains technique to estimate the expected number of elementary evolutionary events (gene duplications and deletions) preceding the formation of gene families of the observed size and found that the mean number of events exceeds the family size by orders of magnitude, suggesting a highly dynamic process of genome evolution. The variance of the time required for the formation of the largest families was found to be extremely large, with the coefficient of variation >> 1. This indicates that some gene families might grow much faster than the mean rate such that the minimal time required for family formation is more relevant for a realistic representation of genome evolution than the mean time. We determined this minimal time using Monte Carlo simulations of family growth from an ensemble of simultaneously evolving singletons. In these simulations, the time elapsed before the formation of the largest family was much shorter than the estimated mean time and was compatible with the timescale of evolution of eukaryotes.The analysis of stochastic BDIMs presented here shows that non-linear versions of such models can well approximate not only the size distribution of gene families but also the dynamics of their formation during genome evolution. The fact that only higher degree BDIMs are compatible with the observed characteristics of genome evolution suggests that the growth of gene families is self-accelerating, which might reflect differential selective pressure acting on different genes. An extremely broad variety of phenomena in physics, biology, and the social sphere is described by power law distributions. The power laws apply to the distribution of the number of links between documents in the Internet, the population of towns, the number of species that become extinct within a year, the number of sexual and other contacts between people, and numerous other quantities -4. In thP(i) ≈ ciγ -where P(i) is the frequency of nodes with exactly i connections or sets with exactly i members, γ is a parameter which typically assumes values between 1 and 3, and c is a normalization constant. Obviously, in double-logarithmic coordinates, the plot of P as a function of i is close to a straight line with a negative slope. Recently, it has been shown that the distributions of several genome-related quantities are best described by the so-called generalized Pareto function: P(i) = c(i + a)γ -where γ > 0, a are parameters [i (i >>a), this distribution is indistinguishable from a power law, but at small i, it deviates substantially, with the magnitude of the deviation depending on a.Mathematically, these distributions are described by the formula: rameters -13. At lPower law distributions and the associated scale-free networks are compatible with the intuitively plausible mechanism of evolution by preferential attachment although other modes of evolution are also possible ,14. Undeduplication obviously is the first elementary process of genome evolution. Genomes and gene families not only grow but often shrink or, probably most of the time, persist in equilibrium. Therefore, duplication must be counter-balanced by the opposite elementary process, gene loss. Again, comparative genomics has shown that gene loss occurs in all species and seems to be extensive in certain lineages, particularly in parasites [innovation. Gene duplication, gene loss, and innovation seem to comprise a reasonable minimal set of elementary events for modeling genome evolution. The only potential major addition could be rearrangement of the gene structure whereby genes accrete or lose domains. However, at least for first approximation modeling, these changes could be covered either by duplication, if they do not yield new genes without detectable relationships to pre-existing families, or by innovation if they do. We should further note that evolutionary analysis of paralogous gene families can be reasonably viewed as a study of the evolution of genomes themselves if all genes are viewed as members of paralogous families, ranging in size (number of members) from 1 to N (the size of the largest family). Of course, one must keep in mind that describing genome evolution in terms of gene duplication, loss, and innovation represents a high level of abstraction, whereby a gene is considered an atomic unit of evolution, and mutation processes occurring within a gene are ignored. However, numerous comparative-genomic studies have shown the utility of the gene-level abstraction both for systematic prediction of the functions of uncharacterized genes using the patterns of their distribution in diverse genomes [However, preferential attachment or other general evolutionary principles associated with power law type distributions and scale-free phenomena do not actually explain the emergence of these phenomena in biologically meaningful terms. A biological explanation involves, at a minimum, identifying the elementary events underlying the evolutionary process and the simplest models of evolution that include these events and are compatible with the observations. Under this logic, families of paralogous genes represent a perfect object for evolutionary modeling. Indeed, for these families, elementary evolutionary processes are defined naturally. By definition, paralogous families evolve by gene duplication. It has been long suspected and, with the advent of genomics, established beyond reasonable doubt that genome evolution proceeds largely by duplication of genes or portions thereof, and even long genomic segments or entire genomes -20. All arasites -27. Fina genomes -31 and f genomes .A natural framework for modeling evolution of gene families is a birth-and-death process, a concept well explored in many physical and chemical contexts . Duplicadeterministic models of genome evolution, which we dubbed BDIMs, after birth (duplication), death (elimination), and innovation  models. We showed that the power law asymptotic of the size distribution of gene families appears if, and only if, birth and death rates of domains in families of sufficiently large size are balanced  and that any power asymptotic with γ ≠ 1 appears only if the per gene birth/death rates depend on the size of the gene family. We showed that the simplest model that adequately approximates the empirical data on gene (domain) family size distributions is the linear 2nd order balanced BDIM.In the previous work , we examnd order balanced stochastic BDIM. Given the published estimates of the rates of gene duplication and loss [Subsequently, we expanded the BDIM framework by introducing stochastic BDIMs, which account not only for the stationary state of the gene ensemble but also for the characteristics of evolution of the system, such as the probability of the formation of a family of the given size before extinction and the mean times of formation and extinction of a family of a given size . We firsand loss , we founand loss .Here, we present a detailed analysis of the properties of different non-linear stochastic BDIMs, including polynomial, rational, and logistic ones, which were obtained by the appropriate transformations of the original linear model. These models generated the same stationary family size distribution, but the stochastic properties of the higher order models were dramatically different from those of the linear BDIM. The mean number of elementary events, duplications and deletions, which are required for the formation of the largest family, decrease monotonically with the increase of the model degree. By contrast, the mean time of formation of a gene family of the given size under a fixed average duplication rate went through a minimum depending on the model degree; typically, the model degree corresponding to this minimum was between 2 and 3. However, even with this optimal degree, the mean times of formation of the largest families in different genomes were unrealistically long.The times of formation and extinction of gene families are random variables with unknown distributions. Therefore it was important to determine the variance of these times and the number of elementary events preceding the formation and extinction of the largest families. We found that the coefficients of variation were very large such that the extreme values of the formation times for the largest family could differ from the mean time by at least two orders of magnitude. Thus, for assessing the feasibility of the formation of the largest families under a given model, the relevant value is not the mean but the minimal time of family formation over the entire ensemble of genes. Using Monte Carlo simulations, we show that the minimal time required for the formation of families of the expected size under BDIMs of the orders between 2–3 is compatible with the timescale of genome evolution.domains for brevity; see [birth, which yields a new member in the same domain family as a result of gene duplication, ii) death, i.e., inactivation and/or deletion of a domain, and iii) innovation, which generates a new, single-member family. Innovation may occur via domain evolution from a non-coding sequence or a non-globular protein sequence, via horizontal gene transfer from another species, or via radical change of a domain after duplication. The rates of elementary events are defined as the probabilities of the respective events during an infinitesimally short time interval [We treat a genome as a "bag" of genes (or, more precisely, portions of genes) encoding protein domains . We also consider virtual, "empty" families that consist of 0 domains. In the stochastic BDIMs, newborn domains are extracted from this class and dead domains return to it. Originally, we examined exclusively the deterministic version of the BDIMs [In a finite genome, the maximum number of domains in a family cannot exceed the total number of domains and, in reality, is probably much smaller. Let he BDIMs . Introduhe BDIMs . In thespi(t) be the frequency of a domain family of size i. Then pi(t) satisfy a system of forward Kolmogorov equations for birth-and-death process (e.g., [Let s e.g., ,46)::pi(t) bep0(t)/dt = -λ0p0(t) + δ1p1(t),dpi(t)/dt = λi-1pi-1(t) - (λi + δi)pi(t) + δi+1pi+1(t) for 0 <i <N,     (1.1)dpN(t)/dt = λN-1pN-1(t) - δNpN(t).dN} and reflecting boundaries in 0 and N. The evolution of individual trajectories of the birth-and-death process X(t), whose state probabilities satisfy the system (1.1), can be described as follows. At the starting time, the system is situated in some initial state x0. The time axis {t ≥ 0} can be divided into intervals [0,τ1), [τ1, τ2), [τ2, τ3) ... such that X(t) is a constant on each interval. If, at the moment τn, the system was situated in the point xn = i, then, in the moment τn+1, the system moves either into the state i+1 with the probability βi = λi/(λi + δi) or into the state i-1 with the probability μi = δi/(λi + δ i). The sojourn time ti = τn+1 - τn between the arrival at the point xn = i and the exit from this point is a random variable independent of the previous history of the system and is distributed exponentially: P{ti ≥ x} = exp(-(λi + δi)x). Note that the random variables ti are independent, and the mean sojourn time, E(ti), in the state i is E(ti) = 1/(λi + δi).Mathematically, (1.1) defines the state probabilities of a birth-and-death process with the finite number of states {0,1,...p0,...,pN defined by the equalities dpi(t)/dt = 0 for 0 ≤ i ≤ N. Let J(i, t) = δipi(t) - λi-1pi-1(t) be the current through the state i in t time moment, J(i) = δipi - λi-1pi-1 be the current in the stationary state. Then the equation for the stationary distribution can be written as J(i+1) - J(i) = 0. As the system is closed, J(0) = 0 and hence J(i) = 0 for all i, such thatProcess (1.1) has a unique stationary ergodic distribution pi / pi-1 = λi-1/δi.N} and reflecting boundaries in states 1 and N:We will consider also the variant of this model with states {1,...p1(t)/dt = -λ1p1(t) + δ2p2(t),dpi(t)/dt = λi-1pi-1(t) - (λi + δi)pi(t) + δi+1pi+1(t) for 1 <i <N,     (1.3)dpN(t)/dt = λN-1pN-1(t) - δNpN(t).dThis model describes the evolution of the size of a domain family that includes an indispensable  gene and is not allowed to go extinct. Similarly, for model (1.3), the ergodic distribution is:χ(i) ≡ λi-1/δi. Let us suppose that, for large i, the following expansion is valid:The ergodic distribution (1.2) (or 1.4) is globally stable and is approached exponentially with respect to time from any initial state. The asymptotic of the ergodic distribution is completely defined by the asymptotic behavior of the function χ(i) ≡ λi-1/δi = is θ (1-γ/i + O(1/i2))     (1.5)s, θ and γ , then, asymptotically, pi ~ θiiγ -. If the process is 2nd order balanced, i.e. s = 0 and θ = 1, then pi ~ iγ-.Then, the asymptotical behavior of the stationary distribution of model (1.1) is completely defined by three parameters: θ and γ . In parThe complete description of all possible asymptotics of the ergodic distributions of model (1.1) under condition (1.5) is given in Mathematical Appendix, Theorem 1  refer to the corresponding formula in the Mathematical Appendix see ). It asslinear BDIM withThe simplest model that shows the generalized Pareto distribution is the λi = λ(i+a), δi = δ(i + b) for i > 0, λ, δ, a and b are constants.     (2.1)The equilibrium distribution of domain family sizes is defined by:λ = δ (θ = 1), the resulting 2nd order balanced linear BDIM has a power asymptotics with γ = 1 + b - a.So, if a and b), this dependence cannot be interpreted as a specific form of interaction between family members. If such interactions are postulated, λi ~ Pn(i) and/or δi ~ Qm(i), where Pn(i), Qm(i) are polynomials on i of the n-th and m-th degrees. The ergodic distribution of the stochastic polynomial BDIM of the form (1.1) and (1.3) is asymptotically the same as that of the originally described deterministic polynomial BDIM [nd order balanced BDIM predict evolution rates that are dramatically greater than those for the linear BDIM.Informally, polynomial BDIMs can be introduced as follows. Under the linear BDIM, the dependence of the birth and death rates on family size is very weak; although each gene "senses" the size of the family , the corresponding BDIM is non-balanced, and equilibrium frequencies have no power asymptotics. Thus, letAs an example, let us consider the quadratic BDIM in more detail. It takes into account the simplest, pairwise interaction between family members, which leads to λi = λ (i2 + r1i + r2), δi = δ(i2 + q1i + q2),     (2.3)λ, δ, rk, qk, k = 1,2 are constants ; equivalently,where λi = λ (i + a)(i + a2), δi = δ (i + b)(i + b2).r1 = a + a2, q1 = b + b2, andThen, χ(i) = λi-1/δi = θ (1 + (r1 - q1 - 2)/i + O(1/i2)), where θ = λ/δ.The quadratic BDIM has equilibrium sizes of domain families (see A.1.6)pi ≈ c2p0 λ0/λθiiρ-2ρ = r1 - q1, c2 = p0 [(Γ (1 + b) Γ (1 + b2)] / [Γ(1 + a) Γ (1 + a2)], andwhere nd order balanced, then pi ~ iρ-2. Note that the asymptotic behavior frequencies pi do not depend on free coefficients r2 and q2 in (2.3), but only on θ and r1 - q1, although the constant c2 could depend on the free coefficients r2, q2.Thus, if the quadratic BDIM is 2nd order balanced, then pi ~ iγ-, .λi = λ(i + a1)/(i + a2)2, δi = δ(i + b1)/(i + b2)2.The rational BDIMs can describe a substantially wider class of birth and death rates compared to polynomial models. In particular, birth rate can have a maximum at some specific value of family size and then decrease with further growth of the size, e.g., as shown in Fig. N, should be finite, although intrinsic features that could determine the value of N so far have not been considered (the impossibility of an infinite genome is self-evident but one would expect a much tighter bound based, e.g., on the limited time and resources available for genome replication and expression). Under the BDIMs described above, birth rate grows monotonically as the family size increases from 1 to N and then abruptly drops to 0 . However, this behavior is an arbitrary simplification of the model and hardly can reflect the actual process of genome evolution.Evidently, the number of size classes of protein families, In population dynamics models, the finiteness of a population size typically results from the "saturation type" of growth: the growth rate tends to 0 as the population size tends to the maximal possible value (see, e.g., ). It seeλ (i) = c3(c1 + i)(N-i), δ(i) = c3i(c2-i), ck > 0, k = 1,2,3, c2 >N.i, namely, 1 <<i <<N and N >> 1.This model produces log-normal and log-series distributions; with the appropriate values of parameters, power low distributions of frequencies also appear, but only for intermediate values of We have shown previously  that theλ*i = λig(i), δ*i = δig(i-1)     (2.4)g(i), i = 0,...N, is a positive function, g(0) = 1, results in a BDIM with the same ergodic distribution of the family sizes as the original one. In particular, modifications of a linear BDIM with g(i) = (i + 1)d-1 or g(i) = (i + 1)d-1(1 - i/(N + c)) define, respectively, wide classes of rational or logistic BDIMs with the same stationary distribution as the original linear BDIM, but with manifestly different dynamic properties.where n before reaching state 0 from an initial state i> 0 is given by formula (A.2.2). In terms of BDIM (1.1), this means that the probability of formation of a family of size n starting from a family of size i before getting to extinction is given by (A.2.2).In is known  that theThe random birth-and-death process (1.1) certainly visits state 0 in the course of time; this means that any domain family will eventually get extinct . Below we compute the mean time to extinction of a family of the given size for different versions of BDIM; the mean time to extinction of the largest family in the given genome is of particular interest.S(n)=inf{t:X(t) = 0|X(0)=n} the time to the first passage of state 0 from the initial state n; S(n) is a random variable for each n. The mean time to extinction of the family of initial size n, E(S(n)), is given by the general formula A.3.2.Let us denote nd order balanced BDIM, the probability that a singleton expands to a family of size n before dying, P(1)(1,n) has the power asymptotics for large n (A.2.5). The values of probabilities P(1)(1,n) for different species are shown in Table -4 - 10-5. The mean time to extinction, E(S(n)), can be calculated using the relation E(S(n)) = 1/λE(1)n, where E(1)n, the mean time to extinction expressed in the 1/λ time units, is given by formula A.3.3 (see Table We have shown previously that, for the linear 2 dying, P1,n) has,n hasnd Var(S(n)) for the linear 2nd order balanced BDIM is Var(S(n)) = 1/λ2W(1)n, where W(1)n can be calculated using the formula (A.3.7). The plot of the coefficient of variation s(1)n = (W(1)n)1/2/E(1)n versus n for different species is shown in Fig. The variance of extinction time The stochastic behavior of the system and its characteristics also can be investigated within the broader framework of rational BDIMs. We will examine models represented as transformed linear BDIM (2.1), withλi = λ(i + a)(i + 1)d-1, δi = λ(i + b)id-1,     (3.1)d ≥ 1 is the model degree. Let us recall that Theorem 1  ~ iγ-. The power γ of this distribution is completely determined by the degree d and the coefficients at id-1. Thus, the model (1.1), (3.1) is representative of all rational BDIMs of the degree d with a given power asymptotic (γ = b - a + 1) of the stationary distribution. Besides, according to Proposition 1, this distribution for model (3.1) is exactly the same as for the corresponding linear model with λi = λ (i + a), δi = λ (i + b), which was studied in detail in [12].where ndix see ) shows We applied formula (A.2.6) g(i) = (i + 1)d-1, to calculate the probability of formation of a family of the given size from a singleton before getting to extinction for the BDIM of degree d, Pd)((1,n). For example, the probabilities P(2)(1,n) and P(3)(1,n) for the quadratic and cubic BDIMs, respectively, are given by this formula with g(i) = i + 1 and g(i) = (i + 1)2, respectively. Figures P(2)(1,n) and P(3)(1,n) on the family size n for different species. The dependence of the probability Pd)((1,N) of the formation of the largest family on the model degree is shown in Fig. with ,n) and P(1,n) ford is calculated using the formula (A.3.4)  where The mean time to extinction for the rational BDIM (1.1), (3.1) with a fixed E*n is the mean time to extinction in the 1/λ time units. Figures E(2)n and E(3)n on n for the quadratic and cubic BDIMs, respectively. Fig. Ed))= 1/λ2Wd)((n), d = 2, 3 for the quadratic and cubic BDIMs, and the coefficient of variation sd)(n = (Wd)(n)1/2/Ed). The results are shown in Figs. Here Figures En and E(i + 1)d-1(1 - i/(N + c)), δi = δ (i + b)id-1(1 - (i - 1)/(N + c)).     (3.2)c as saturation boundary. The shape of λi essentially depends on the value of c , (3.2) is a transformation (2.9) of the linear BDIM using the function:i) = (i + 1)d-1(1 - i/(N + c), c = const ≥ 0.     (3.3)g(i + 1)d-1, δi = δ (i + b)id-1, but with small c, we can observe some new effects when the family size approaches N.The stationary distribution of family size frequencies for the logistic model (1.1), (3.2) is exactly the same as that for corresponding linear BDIM but the stochastic properties are different and close to the rational models, and essentially depend on the boundary g(i) is given by (3.3). The dependence of this probability on the model degree d under a fixed large value of the boundary c~N is similar to that for the corresponding rational models but differs under a small c; Fig. c = 1.The probability of formation of a family of a given size from a singleton before getting to extinction for the logistic BDIM is calculated using the general formula (A.2.6) where the function Ed). Fig. T(j, n) = inf{t: X(t) = n|X(0) = j} the time to the first passage of state n from the initial state j; T(j, n) is a random variable for each j, n. The mean time to the first passage for BDIM (1.1), m(j, n) = E(T(j, n)), can be calculated using the formula m(j, n) = m0(j, n) + m1(j, n). Here the term m0(j,n) is the mean time elapsed before the system leaves the 0 state for the last time, and the term m1(j,n) is the mean time of formation of a family of size n from a singleton after its last "resurrection" (see formulas (A.4.1) for details). Below we examine only the mean family formation time from an essential singleton (model (1.3)).Let us denote n from a singleton for different species [M(1)(1,n) = 1/λ M(1)n, where M(1)n, the mean formation time in 1/λ units is calculated using the formula (A.4.6)Previously, we determined the mean time of formation of a family of size  species . For theλ time units to years is considered in s.6 of the Mathematical Appendix [see E(T(1, n)) in years is calculated using the formula (A.6.4) and the current empirical estimates of the gene duplication rate [E(T(1, n)) for different species are shown in Fig. The transition from the 1/ndix see . The meion rate . Plots omean time of formation of a family of size n for different species, the question arises how accurately is the time T(i, n) of the first random passage through the threshold n predicted by the mean value. To address this problem, we estimated the variance of the time of family formation, Var(T(i, n)) using the general formulas (A.5.2) for model (1.1) and (A.5.3) for model (1.3), respectively. For the linear BDIM, the variance of the formation time for a family of size n from an essential singleton, V(1)n, is given by the formula (A.5.5). A more important and informative characteristic, which is independent on the model parameter λ, is the coefficient of variation, which is equal to . The coefficient of variation of the formation time of a family of size n from a singleton, σd)(n = (Vd)(n)1/2/Md) for the BDIM of degree d is the most relevant value. The plots of σ(1)n versus n for the linear model and for different species are shown in Fig. Once we computed the rdu ≈ 2 × 10-8 duplications/gene/year [λ units, we obtain M(1) ~ 1013 - 1014 yrs, a completely unrealistic time estimate. The mean family formation times given by the linear BDIM would become realistic only if the recent analyses underestimated the gene duplication rate by a factor of ~104, which does not seem plausible. Thus, the linear BDIM cannot provide an adequate description of genome evolution, at least when only the mean time of family formation is considered. The variance of the family formation time is extremely large (the coefficient of variation is ~102), and, accordingly, large deviations from the mean time, more to two orders of magnitude, are possible. However, even taking this into account, the family formation times predicted by the linear BDIM are far longer than the time allotted for life's evolution of earth. In the next section, we consider non-linear, higher order models that have the potential to yield shorter mean times of family formation.The coefficients of variation were very large for all species  to exprn for the polynomial BDIMs is E(T(1,n)) = 1/λ M*n where M*n, the mean time of formation in 1/λ units can be calculated using the formulas (A.4.9)The mean time of formation of families from an essential singleton (or after the last "resurrection" of a family) depending on the family size Fig. n can be calculated using the formula (A.5.6), with g(j)=j+1 for the quadratic BDIM and g(j) = (j + 1)2 for the cubic BDIM, respectively. The dependence of the coefficient of variation σ(2)n = (V(2)(1,n))1/2/M(2) on the family size for the quadratic BDIM is shown in Fig. The variance of formation time of a family of the size Although the variance of family formation times for the quadratic BDIM is approximately 5 orders of magnitude less than that for the linear BDIM, the values of the coefficient of variation for quadratic BDIM are about 1.3–1.5 times greater than those for the linear BDIM. Thus, the actual formation time for the largest family could differ from the mean value by several orders of magnitude with a high probability. Figures under the same value of the parameter λ [11 yrs. This value is close to the minimum possible time of family formation that can be calculated using the duplication rate estimates of Lynch and Conery [We have shown previously that the cubic model shows extremely high evolution rate comparatively with the linear and even quadratic models ameter λ . On the d Conery  and non-n from a singleton under a fixed model degree d, Md), for the rational BDIM (1.1),(3.1), is calculated using the formula (A.4.9). A comparison of the mean time of formation and extinction for rational BDIMs reveals an interesting property of non-linear BDIMs: for any given family size n, there exists such a model degree that the times of family formation and extinction are equal ,.1, is cad results in indefinite decrease of the family formation time expressed in 1/λ time units , does not decrease indefinitely, but has a minimum at the model degree between 2 and 3  e units  and (A.5.6). Although the variance of formation times for the quadratic and, especially, for the cubic BDIM is several orders of magnitude less than that for the linear BDIM, the coefficients of variation for both formation and extinction time increase with the model degree Table . These cλ units) of a family of size n from an essential singleton for the logistic BDIM (1.3), (3.2) under fixed d is calculated using formula (A.4.9). Fig. Md), on the family size n for different model degrees d under the fixed saturation boundary c = 1, and Fig. λ. However, when this inner model parameter is excluded and the mean time of family formation is expressed in years according to formula (A.6.5), then we again face a restriction that does not allow indefinite shortening of the family formation time, Td)} instead of the original BDIM. The embedding chain {Yn} for a particular BDIM is a random walk with discrete time on the same set of states and transition probabilities pi,i+1 = βi = λi/(λi + δi), pi,i-1 = μi = δi/(λi + δi) and pij = 0 for all other cases .i to the state i+1 (or i-1) corresponds to the duplication (or deletion) of a domain in a family of size i. The only difference between the original birth-and-death process and the embedding chain is that the sojourn time for the embedding chain is equal to 1 for any state i instead of 1/(λi + δi). The ratio βi/μi (= λi/δi) characterizes the trend of family evolution from the state i, i.e., is the family more likely to grow or to shrink; for a symmetric random walk, βi/μi = 1 for all i. The dependence of the ratio βi/μi on i for different rational and logistic embedded chains is shown in Figures βi/μi ≈ 1 for large i; for the logistic models, βi/μi ≈ 1 for 0 <<i <<N . Thus, the behavior of the embedding chain is similar to the behavior of the symmetric random walk in the corresponding subsets of states. Informally, the plots in Figures The transition from the state fn, is computed using formulas (A.7.5)-(A.7.7). The plots in Figures fn on the family size for different species for the linear and quadratic models, respectively. The mean number of elementary events before the extinction of a family of the given size, en, is computed using formulas (A.7.13)-(A.7.15) and Figures The mean number of elementary events before the formation of a family of the given size, ulas A.7.3-, and the optimum values of d was lower by ~0.5 , it is reasonable to view these unique genes as resulting from innovation. For genomes to maintain equilibrium, the combined rates of duplication and innovation over the entire ensemble of gene families should equal the rate of gene loss, at least when averaged over long time spans. The observed distribution of family sizes, which asymptotically tends to a power law, dictates a much more specific connection between the gene birth and death rates, namely, the second order balance. It should be noted that this form of balance does not amount to particularly fine tuning of the gene birth and death rates. The only requirement is that these rates tend to the same value when the family size tends to infinity according to the condition (1.5). In contrast, for small families, the rates may substantially differ, without significantly changing the shape of equilibrium distribution.Here and in the previous publications ,43,50, wThe incentive to examine BDIMs in detail stems from at least two fundamental questions: i) are the above elementary evolutionary mechanisms sufficient to account for the empirically observed characteristics of genomes, ii) what is the contribution of natural selection to the general, quantifiable features of genomes, such as the size distribution of gene families. The analysis of BDIMs starts to provide some answers, albeit preliminary ones. The critical observation made in the course of BDIM analysis was that different versions of these models could be readily distinguished on the basis of goodness of fit to the empirical data. This being the case, we found that the simplest possible model, in which all paralogs are considered independent, is incompatible with the data. Thus, turning to the first of the above questions, we had to conclude that, in addition to the three elementary processes, "something else" was required to model genome evolution. This "something" is the dependence or "interaction" between gene family members which results in self-accelerating family growth. In order to account for the observed stationary distribution of family sizes, it is sufficient to introduce a very weak dependence as embodied in the linear BDIM. However, when we switched from the deterministic to the stochastic version of BDIMs, which provide for the possibility of analysis of the dynamics of the systems evolution, we found that evolution under the linear BDIM was much too slow to account for the emergence of the large families of paralogs found in all genomes during the time of life's evolution. Only higher order BDIMs, with degrees between 2 and 3, i.e., with "strong interactions" between family members were found to provide for sufficiently fast evolution to be compatible with the real biological timescale.Obviously, these findings beg the question: what is the nature of the mysterious "interactions" between paralogs? Although, on some occasions, paralogous protein do form physical complexes or interact functionally, the situation when such interaction does not exist is much more common. Therefore, the "interactions" in our models should not be perceived literally. This brings us to the second of the above major problems. BDIMs do not explicitly include the notion of selection. However, the simplest interpretation of the virtual interactions implied by the higher order BDIMs seems to be that these reflect differential tendencies of genes to form paralogous families of different sizes depending on the intensity of selection. Recent studies have shown that evolutionary fixation of gene duplications is linked to the evolutionary rates of genes. Specifically, duplications of slowly evolving genes, i.e., those that are subject to stronger purifying selection, are fixed more often ,55. The In this work, we extended our analysis of stochastic Birth, Death and Innovation Models (BDIMs) of gene family evolution and showed that:• the behavior of logistic BDIMs models, in which birth/death rates are limited for the largest families, is essentially the same as that of previously investigated BDIMs that included no such limitationd ≈ 2.7, the mean time of the largest family formation is orders of magnitude greater than any realistic estimates based on the timescale of life's evolution;• the mean time required for the growth of large families is limited by the overall number of duplications and does not increase indefinitely with the increase of the model degree but instead passes through a minimum; even under the best-case scenario, which corresponds to a non-linear rational BDIM with • using the embedding chains technique, we estimated the expected number of elementary evolutionary events (gene duplications and deletions) preceding the formation of gene families of the observed size; the mean number of events exceeds the family size by orders of magnitude, suggesting a highly dynamic process of genome evolution;• the variance of the time required for the formation of the largest families is large (coefficient of variation >> 1), which means that some families might grow much faster than the mean rate; thus, the minimal time required for family formation is more relevant for a realistic representation of genome evolution than the mean time;d ≈ 2.2).• Monte Carlo simulations of family growth from an ensemble of simultaneously evolving singletons show that the time elapsed before the formation of the largest family was much shorter than the estimated mean time and approached realistic values (2.2 ± 0.5 Ga for the non-linear rational BDIM with GPK developed most of the mathematical formalism and wrote the draft of the mathematical part of the manuscript; YIW performed the imitation modeling and wrote the draft of the corresponding part of the manuscript; FSB derived some of the mathematical statements; EVK contributed to the inception of the work and the formulation of the models, gave the biological interpretation of the results, wrote the background and discussion sections and extensively edited the entire manuscript.This additional file includes proofs of some of the mathematical statements contained in the main text as well as accessory mathematical formulations.Click here for file


C. elegans. Feeding of this RNAi library to the standard wild-type laboratory strain Bristol N2 detected phenotypes for approximately 10% of the corresponding genes. To increase the number of genes for which a loss-of-function phenotype can be detected, we undertook a genome-wide RNAi screen using the rrf-3 mutant strain, which we found to be hypersensitive to RNAi. Feeding of the RNAi library to rrf-3 mutants resulted in additional loss-of-function phenotypes for 393 genes, increasing the number of genes with a phenotype by 23%. These additional phenotypes are distributed over different phenotypic classes. We also studied interexperimental variability in RNAi results and found persistent levels of false negatives. In addition, we used the RNAi phenotypes obtained with the genome-wide screens to systematically clone seven existing genetic mutants with visible phenotypes. The genome-wide RNAi screen using rrf-3 significantly increased the functional data on the C. elegans genome. The resulting dataset will be valuable in conjunction with other functional genomics approaches, as well as in other model organisms.RNA-mediated interference (RNAi) is a method to inhibit gene function by introduction of double-stranded RNA (dsRNA). Recently, an RNAi library was constructed that consists of bacterial clones expressing dsRNA, corresponding to nearly 90% of the 19,427 predicted genes of  The screen suggested functions for 393 genes for which no RNAi-mediated phenotype was known. The comparison with similar screens in worms has general implications for RNAi experiments Caenorhabditis elegans was constructed (C. elegans using this RNAi library or other techniques (RNA interference (RNAi) is targeted gene silencing via double-stranded RNA (dsRNA); a gene is inactivated by specific breakdown of the mRNA . It is astructed . Upon festructed . Other gchniques . These srrf-3, a putative RNA-directed RNA polymerase (RdRP), resulted in increased sensitivity to RNAi . To explore the variability of global RNAi screens, we performed the rrf-3 screen twice for Chromosome I and carried out a Chromosome I screen with wild-type. These were cross-compared and also compared to the results of the wild-type screen of rrf-3 consistently allowed detection of more phenotypes than wild-type. In addition, we found that there is a significant screen-to-screenvariability (10%–30%).We have conducted a genome-wide RNAi screen using the rrf-3 and found RNAi phenotypes for 456 bacterial clones. We compared these data to those obtained by rrf-3 . These data show that large-scale RNAi screens done within the same laboratory and by the same investigators also give variable results. A few examples of variable RNAi results are shown in It is possible that different laboratories or investigators have slightly different results. However, when we compare the results that we obtained with two independent screens of Chromosome I using e second D. Among In conclusion, we find that RNAi results from different laboratories and from experiments done in the same laboratory vary from 10% to 30%. This appears to be due to a high frequency of false negatives in each RNAi screen, even when the same method is used in the same laboratory.rrf-3 strain, we next screened the complete RNAi library with rrf-3 mutant animals. We obtained results for 16,401 clones and detected phenotypes for 2,079 (12.7%). Of these, we identified phenotypes for 625 clones for which no phenotype was reported in the generating phenotypes in both screens (Based on the positive results of the Chromosome I screen using the  screens . In addi screens  or Kamat screens  found phrrf-3 screen were retested. Subsequently, the phenotypes of the clones corresponding to Chromosomes II to X that were not confirmed by this repetition were tested once more. In this way, the clones specific for the rrf-3 screen had two chances to be confirmed. Of the 625 clones for which no phenotype was found in the The clones for which we only detected an RNAi phenotype once and that were specific for the rrf-3 that remained unconfirmed could be confirmed by RNAi phenotypes detected with other clones of the RNAi library corresponding to the same gene or by other laboratories using different RNAi methods. For example, for the clones corresponding to the predicted genes F56D1.1 (a member of the zinc finger C2H2-type protein family) and F27C8.6 (a member of the esterase-like protein family), we detected sterile progeny (Stp) and embryonic lethality (Emb), respectively; these were also found by gpc-2, hlh-8, and unc-84. This suggests that many of the unconfirmed phenotypes reflect true gene functions.Some of the RNAi phenotypes only found with rrf-3, we first assayed the rate of false positives in the total dataset . In the assay used by rrf-3 data is similarly low (0 of 152 genes).To validate the results obtained using rrf-3 data were confirmed by refeeding only if there was no phenotype detected in the N2 screens by rrf-3 and the N2 screens, we used the Chromosome I datasets, where phenotypes were confirmed independently for the two strains. Of 75 genetic loci on Chromosome I, rrf-3 (rrf-3 dataset (excluding the 202 unconfirmed phenotypes), we detected the published phenotype for 54% of 397 selected loci, compared to 52% for N2 , we detected 31 using rrf-3, compared to 33 for N2. Thus, detection of essential genes was similar in the two strains, but detection of postembryonic phenotypes was improved with rrf-3. Finally, for the whole genome using rrf-3, we reproducibly detected the published phenotypes for 34 genetic mutants for which no RNAi phenotype was reported in the N2 screens . By comparison, published phenotypes were detected for 23 loci only with N2  (see rrf-3 particularly improves detection of genes with postembryonic mutant phenotypes, a class that is poorly detected using wild-type N2.We next asked whether using the pes) see . We concrrf-3 dataset is the high number of clones where a slow or arrested growth (Gro/Lva) defect was induced, without associated embryonic lethality or sterility. Overall, 619 clones induced a Gro/Lva defect using rrf-3, compared to 276 for N2, whereas the number of essential genes detected was similar (1,040 versus 1,170, respectively). In addition, in the confirmed set of 423 clones with rrf-3-specific phenotypes, Gro/Lva defects are the largest category (42%), whereas this is only 18% for N2, with the largest category being essential genes (49%). These data suggest that rrf-3 might particularly enhance detection of genes that mutate to a slow-growth phenotype; we cannot easily test this hypothesis, as there are currently few known loci with this mutant phenotype. In some cases, a Gro/Lva phenotype was seen in rrf-3, whereas a different phenotype was seen in N2 . This suggests that some of the Gro/Lva phenotypes detected are due to incomplete RNAi of an essential gene  or by a stronger RNAi effect (where no growth defect was seen in N2). In addition, it is possible that some of the Gro/Lva phenotypes detected are synthetic effects of using the rrf-3 mutant strain.A striking feature of the rrf-3 RNAi supersensitive strain in large-scale screens increases the percentage of clones for which it is possible to detect a phenotype. Detection of postembryonic phenotypes is particularly increased, whereas detection of essential genes is similar in rrf-3 and N2. In addition, using rrf-3, there is a high rate of induction of Gro/Lva defects.To summarise, using the http://www.wormbase.org, the Internet site for the genetics, genomics, and biology of C. elegans). Second, we searched for clones near the defined map positions that, when fed to N2, rrf-3, or both, gave phenotypes corresponding to the phenotypes of the genetic mutants. For most genetic mutants, more than ten clones with a similar phenotype were found in the interval to which the genetic mutant was mapped. However, for 21 genetic mutants, only one or a few candidate clones were found. The genes corresponding to these clones were subsequently sequenced in the genetic mutant to determine whether a mutation was present. In total, we sequenced 42 predicted genes for the 21 genetic mutants  by RNAi causes uncoordinated movement  using rrf-3. For 13 genes, With this genome-wide RNAi screen using the hypersensitive strain rrf-3 strain has an increased incidence of males (Him) and displays slightly increased embryonic lethality and a reduced brood size . In particular, a large number of clones induced a developmental delay phenotype using rrf-3. Synthetic effects cannot be excluded without investigating genetic mutants. Again, variability in the efficiency of RNAi will also contribute to these differences, and a small portion may be false positives. In general, the few false positives that occur in the screen are most likely due to experimental errors, whereas the false negatives are due to reduced efficiency of the RNAi. Finally, differences between rrf-3 and N2 do not only involve the absence and presence of an RNAi phenotype, but also differences in the phenotypes for clones that did induce phenotypes in both screens . For example, we detected for unc-112 a 100% embryonic lethal (Emb) phenotype with rrf-3, whereas gon-1 a 100% Emb phenotype and other phenotypes with N2, while we did not detect an Emb phenotype with rrf-3.When an RNAi phenotype is detected with What could be the source of the interexperimental variation of RNAi? Different phenotypes for the same gene can possibly occur owing to slight differences in the developmental stage at which the animals are exposed to dsRNA and owing to changes in temperature during the experiment. However, this probably does not account for the differences we see, as we always used animals of the same larval stage (L3/L4) and used incubators for constant temperature. It was shown previously that the level of induction of dsRNA production by isopropylthio-β-D-galactoside (IPTG) can modify the penetrance of the RNAi phenotype . TherefoC. elegans, since these mutants have been extensively used as visible markers in linkage studies.The RNAi data can be a useful starting point for many new experiments, such as positional cloning of genetic mutants. By sequencing candidate genes based on the RNAi phenotypes, we identified the causal mutation in seven genetic mutants. Identification of these mutated genes gives insight into the biological process in which they are involved. In addition, cloning of these genes increases the resolution of the genetic map of rrf-3 will be submitted to WormBase, annotated as confirmed or unconfirmed. There the data can be evaluated in the context of information on gene structure, expression profiles, and other RNAi results.The complete set of RNAi phenotypes detected for the 2,079 clones using C. elegans strains: Bristol N2, NL4256 rrf-3(pk1426), CB767 bli-3(e767), MT1141 bli-3(n259), CB518 bli-5(e518), BC649 bli-5(s277), CB1158 dpy-4(e1158), CB1166 dpy-4(e1166), CB14 dpy-6(e14), CB4452, dpy-6(e2762), F11 dpy-6(f11), CB12 dpy-9(e12), CB1164 dpy-9(e1164), BC119 dpy-24(s71), CB3497 dpy-25(e817), MT1222 egl-6(n592), MT1179 egl-14(n549), MT1067 egl-31(n472), MT151 egl-33(n151), MT171 egl-34(n171), egl-34(e1452), MQ210 mau-4(qm45), CB754 rol-3(e754), BC3134 srl-2(s2507dpy-18(e364); unc-46(e177)rol-3(s1040), CB713 unc-67(e713), CB950 unc-75 (e950), HE177 unc-94(su177), HE33 unc-95(su33), HE151 unc-96(su151), unc-96(r291), HE115 unc-100(su115), MT1093 unc-108(n501), and MT1656 unc-108(n777).We used the following rrf-3 strain was used: after transferring L3- to L4-staged hermaphrodites onto the first plate, we left them for 48 h at 15°C instead of 72 h and then plated single adults onto other plates seeded with the same bacteria. Furthermore, we did not remove the mothers from the second plates. The phenotypes assayed are these: Emb , Ste (sterile), Stp (sterile progeny), Brd (low broodsize), Gro (slow postembryonic growth), Lva , Lvl , Adl , Bli (blistering of cuticle), Bmd , Clr (clear), Dpy (dumpy), Egl (egg-laying defective), Lon (long), Mlt (molt defects), Muv (multivulva), Prz , Pvl (protruding vulva), Rol (roller), Rup (ruptured), Sck (sick), Unc (uncoordinated) Thin and Pale. Emb was defined as greater than 10% dead embryos for N2 and greater than 30% dead embryos for rrf-3. Ste required a brood size of fewer than ten among fed N2 worms and fewer than five among rrf-3. Each postembryonic phenotype was required to be present among at least 10% of the analysed worms.RNAi was performed as described elsewhere  with minhttp://primers.niob.knaw.nl/). Sequence reactions were done using the ABI PRISM Big Dye terminator sequencing kit  and were analysed on the ABI 3700 DNA analyser. The coding sequence and the 5′- and 3′-untranslated region (about 500 bp upstream and downstream of the coding sequence) of the predicted genes, as annotated in WormBase, was analysed for mutations by sequencing amplified genomic DNA of the genetic mutants see . Nested C. elegans using the BLAST program (http://www.sanger.ac.uk/Projects/C_elegans/blast_server.shtml) or analysed using the PolyPhred program (available from http://droog.mbt.washington.edu/PolyPhred.html).Sequences were compared to the genomic sequence of Table S1(482 KB PDF).Click here for additional data file.Table S2(188 KB PDF).Click here for additional data file.Table S3(25 KB DOC).Click here for additional data file.http://www.wormbase.org).RNAi data from this study will be submitted to WormBase (